The Biophysical Toolbox: a Biophysical Modelling Tool Developed within the IWRAM-DSS

With rapid intensification of agricultural catchments in northern Thailand a suite of environmental issues have surfaced. The Integrated Water Resources Assessment and Management (IWRAM) project was instigated in response to these issues. The project developed a Decision Support System for the exploration of biophysical and socio-economic impacts of water resources use option. The IWRAM-DSS is comprised of a 'Biophysical Toolbox' that can be implemented alone or an 'Integrated Toolbox' that links socioeconomic models with the biophysical toolbox to explore economic trade-offs and impacts of various scenarios. The Biophysical Toolbox is comprised of three modules - the CATCHCROP crop model, a hydrologic module based upon the IHACRES rainfall-runoff model, and a Universal Soil Loss Equation (USLE) approach modified to suit conditions in northern Thailand. This working paper describes and implements the Fortran 77 version of the Biophysical Toolkit developed jointly by Dr. Barry Croke and Wendy Merritt. A Java version of the model has been coded by Dr. Claude Dietrich and Nick Ardlie, however this version has not been linked with the economic model as part of the fully integrated IWRAM-DSS

Sensitivity testing of a biophysical toolbox for exploring water resources utilisation and management options

This paper investigates the sensitivities of model outputs to model parameter values within a Biophysical Toolbox developed as part of a Decision Support System (DSS) for integrated catchment assessment and management of land and water resources in the highland regions of northern Thailand. The toolbox contains a hydrological module based upon the IHACRES rainfall-runoff model, a crop model (CATCHCROP), and an erosion model (USLE) modified to suit conditions in northern Thailand. Emphasis in the development of the individual models within the Biophysical Toolbox was placed upon limiting model complexity. Limited data availability commonly restricts the complexity of the model structure that can justifiably be used to model natural systems. The challenge under conditions with limited data is then to strike a balance in the model(s) between statistical rigour and model complexity. Once encompassed within the Biophysical Toolbox, linkages between the models increase the complexity of the system, despite the relative simplicity of the individual models. Consequently, the impacts of outputs from individual models on the outputs of other models deserve considerable attention. Understanding model sensitivity is of particular importance where there is a lack of data with which to support or adequately verify model behaviour. Sensitivity analysis potentially allows the identification of model components that require attention in terms of improved parameter estimation or improvement in model structure. Preliminary testing of the individual models within the Biophysical Toolbox has been reported previously within the literature and the Biophysical Toolbox as a whole has been described. This paper explores sensitivities within the Biophysical Toolbox, targeting in particular the identification of components of the toolbox in which sensitivities are propagated throughout the model

Use of catchment attributes to identify the scale and values of distributed parameters in surface and sub-surface conceptual hydrology models

Improved prediction for problems in catchment hydrology requires an ability to spatially disaggregate and connect surface and sub-surface components. This paper considers two hydrological models for use in such disaggregation and coupling: a lumped conceptual rainfall-runoff model (IHACRES) and a physics based conceptual groundwater discharge model. Smaller gauged catchments in the vicinity can be used to regionalise and parameterise the coupled model using catchment attributes prior to running the model in a larger catchment with fewer gauges. Regionalisation in gauged catchments at appropriate scales would capture the uncertainty of the relationships between catchment attributes and model parameter values, including the upper and lower boundary of parameter values. In an ungauged and disaggregated catchment, its landscape attributes would be inserted into the regional relationships to provide the parameter bounds for constraining the proposed coupled model. The aim of this catchment disaggregation is to be able to improve on previous catchment or sub-catchment recharge-discharge models, so that modelling can be carried out at the management scale