IWRAM: An integrated toolbox for considering impacts of development and land use change in Northern Thailand

The IWRAM Decision Support System was developed to consider economic, environmental, and sociocultural trade-offs involved with resource competition and development in the Mae Chaem catchment in Northern Thailand. IWRAM contains two modelling toolboxes utilising a nodal network structure for catchment analysis: a Biophysical Toolbox, for considering the biophysical (erosion, streamflow, crop) implications of 'painted on' land use scenarios; and, an Integrated Modelling Toolbox, which links models of household decision making with the biophysical toolbox to allow for consideration of socioeconomic and environmental trade-offs of many development and policy scenarios. This paper describes the Integrated Modelling Toolbox within the IWRAM system. Links between household decision models, a socioeconomic impacts model and the biophysical toolbox are described and results for a number of forest encroachment scenarios are demonstrated using key indicators of social, economic and environmental performance. The potential for reapplication of the modelling framework to a large number of catchment situations is also discussed. (Résumé d'auteur

Prediction of Monthly Discharge in Ungauged Catchments Under Agricultural Land Use in the Upper Ping Basin, Northern Thailand

The present paper describes a methodology proposed for surface runoff modelling in gauged and ungauged subcatchments of Northern Thailand. Gauged catchments are modelled using calibration against measured flow data, whereas streamflow in the ungauged subcatchments is simulated by a disaggregation procedure utilising measured streamflow data from a larger gauged catchment in which the ungauged subcatchment may be nested. The disaggregation technique is based on the assumption that the streamflow contribution from each subcatchment to the total catchment yield is proportional to a ratio of the catchment's area and its average slope. The Mae Chaem catchment in the Upper Ping River basin was selected as a case study for applying the approach. The model testing performed in two subcatchments, where the modelled streamflow was compared with the measured data, showed that the first pass approach algorithm provides the accuracy of 13-17% of the relative error for the monthly time step

Practical identifiability analysis of environmental models

Identifiability of a system model can be considered as the extent to which one can capture its parameter values from observational data and other prior knowledge of the system. Identifiability must be considered in context so that the objectives of the modelling must also be taken into account in its interpretation. A model may be identifiable for certain objective functions but not others; its identifiability may depend not just on the model structure but also on the level and type of noise, and may even not be identifiable when there is no noise on the observational data. Context also means that non-identifiability might not matter in some contexts, such as when representing pluralistic values among stakeholders, and may be very important in others, such as where it leads to intolerable uncertainties in model predictions. Uncertainty quantification of environmental systems is receiving increasing attention especially through the development of sophisticated methods, often statistically-based. This is partly driven by the desire of society and its decision makers to make more informed judgments as to how systems are better managed and associated resources efficiently allocated. Less attention seems to be given by modellers to understand the imperfections in their models and their implications. Practical methods of identifiability analysis can assist greatly here to assess if there is an identifiability problem so that one can proceed to decide if it matters, and if so how to go about modifying the model (transforming parameters, selecting specific data periods, changing model structure, using a more sophisticated objective function). A suite of relevant methods is available and the major useful ones are discussed here including sensitivity analysis, response surface methods, model emulation and the quantification of uncertainty. The paper also addresses various perspectives and concepts that warrant further development and use

Techniques for assessing the performance of a landscape-based sediment source and transport model: sensitivity trials and physical methods

Widespread degradation of aquatic habitat and water quality has occurred since European settlement of Australia. Repairing this degradation is expensive and hence on-ground management needs to be carefully focussed. The Sediment River Network model, SedNet, used for the estimation of the sources and transport of sediment spatially and at catchment scales, potentially provides a useful tool to assist land managers in focusing this work. The complete model, whilst broadly applied has not been systematically tested to assess its accuracy or sensitivity to its various model components. The aim of this paper is to propose a framework for such testing. Results from the work will be used to prioritise data acquisition, and improve the structure and parameterisation of the model where necessary. The research is also particularly relevant for shifting application of the model from continental to catchment scales. The testing will comprise two components - sensitivity assessment and accuracy assessment. This paper provides a brief introduction to the SedNet model and a framework for assessing the model. Examples of sensitivity assessment and accuracy assessment are provided and discussed

A comparative analysis of precipitation estimationmethods for streamflow prediction

Surface hydrologic models are widely used for streamflow prediction, forecasting and for understanding hydrologic processes. They are also an important tool for contributing to the resolution of wider resource and environmental issues, providing information to support policies and decisions for water resource management. Precipitation is a key input to hydrologic models and is however also the major source of predictive uncertainty. Whilst station-based observed precipitation data can be adequate for hydrologic modelling in small catchments, they may not be sufficient for large catchments, in particular for large catchments with a mountainous terrain. Areal estimation of precipitation is a potential option to provide more precise precipitation input to models for large catchments. Conventionally, for areal precipitation estimation, station-based precipitation data are interpolated across the model domain using various methods, including Spline fitting, Inverse Distance Weighting (IDW) and the classical Thiessen Polygon, which are among the more popular and commonly used methods. Different precipitation interpolation methods will affect the spatial and temporal variability of areal precipitation inputs, resulting in different uncertainties when used to help calibrate a surface hydrologic model. This paper investigates the effect of the above three types of precipitation interpolation methods (ANUSPLIN surface, IDW surface and Thiessen polygon) on streamflow predictions. The Chaohe basin located in northern China is selected as the study area. It is an important headwater of the Miyun Reservoir which provides drinking water to Beijing and surrounding townships. Three lumped, surface hydrologic models (GR4J, IHACRES and Sacramento) are selected to study the accuracy and predictive uncertainty of these three types of precipitation interpolation on daily streamflow. The models were calibrated separately using discharge observations from three gauges in the basin. The results show that the ANUSPLIN surface interpolation performs the best overall under various combinations of conditions. The IDW surface also performs well in the upper and middle basin but the Thiessen polygon is inferior to the other two methods. The comparison of the three hydrologic models shows that IHACRES and Sacramento perform better than GR4J. The best combination is areal rainfall estimated using the ANUSPLIN derived surface with the IHACRES model in the case study catchments, though the Sacramento model is a close second.This work was supported by the National Natural Science Foundation of China (No. 41271004)

Modeling Water Quality in Watersheds: From Here to the Next Generation

In this synthesis, we assess present research and anticipate future development needs in modeling water quality in watersheds. We first discuss areas of potential improvement in the representation of freshwater systems pertaining to water quality, including representation of environmental interfaces, in-stream water quality and process interactions, soil health and land management, and (peri-)urban areas. In addition, we provide insights into the contemporary challenges in the practices of watershed water quality modeling, including quality control of monitoring data, model parameterization and calibration, uncertainty management, scale mismatches, and provisioning of modeling tools. Finally, we make three recommendations to provide a path forward for improving watershed water quality modeling science, infrastructure, and practices. These include building stronger collaborations between experimentalists and modelers, bridging gaps between modelers and stakeholders, and cultivating and applying procedural knowledge to better govern and support water quality modeling processes within organizations

A framework for characterising and evaluating the effectiveness of environmental modelling

Environmental modelling is transitioning from the traditional paradigm that focuses on the model and its quantitative performance to a more holistic paradigm that recognises successful model-based outcomes are closely tied to undertaking modelling as a social process, not just as a technical procedure. This paper redefines evaluation as a multi-dimensional and multi-perspective concept, and proposes a more complete framework for identifying and measuring the effectiveness of modelling that serves the new paradigm. Under this framework, evaluation considers a broader set of success criteria, and emphasises the importance of contextual factors in determining the relevance and outcome of the criteria. These evaluation criteria are grouped into eight categories: project efficiency, model accessibility, credibility, saliency, legitimacy, satisfaction, application, and impact. Evaluation should be part of an iterative and adaptive process that attempts to improve model-based outcomes and foster pathways to better futures

Eight grand challenges in socio-environmental systems modeling

Modeling is essential to characterize and explore complex societal and environmental issues in systematic and collaborative ways. Socio-environmental systems (SES) modeling integrates knowledge and perspectives into conceptual and computational tools that explicitly recognize how human decisions affect the environment. Depending on the modeling purpose, many SES modelers also realize that involvement of stakeholders and experts is fundamental to support social learning and decision-making processes for achieving improved environmental and social outcomes. The contribution of this paper lies in identifying and formulating grand challenges that need to be overcome to accelerate the development and adaptation of SES modeling. Eight challenges are delineated: bridging epistemologies across disciplines; multi-dimensional uncertainty assessment and management; scales and scaling issues; combining qualitative and quantitative methods and data; furthering the adoption and impacts of SES modeling on policy; capturing structural changes; representing human dimensions in SES; and leveraging new data types and sources. These challenges limit our ability to effectively use SES modeling to provide the knowledge and information essential for supporting decision making. Whereas some of these challenges are not unique to SES modeling and may be pervasive in other scientific fields, they still act as barriers as well as research opportunities for the SES modeling community. For each challenge, we outline basic steps that can be taken to surmount the underpinning barriers. Thus, the paper identifies priority research areas in SES modeling, chiefly related to progressing modeling products, processes and practices

Effective modeling for integrated water resource management: a guide to contextual practices by phases and steps and future opportunities

The effectiveness of Integrated Water Resource Management (IWRM) modeling hinges on the quality of practices employed through the process, starting from early problem definition all the way through to using the model in a way that serves its intended purpose. The adoption and implementation of effective modeling practices need to be guided by a practical understanding of the variety of decisions that modelers make, and the information considered in making these choices. There is still limited documented knowledge on the modeling workflow, and the role of contextual factors in determining this workflow and which practices to employ. This paper attempts to contribute to this knowledge gap by providing systematic guidance of the modeling practices through the phases (Planning, Development, Application, and Perpetuation) and steps that comprise the modeling process, positing questions that should be addressed. Practice-focused guidance helps explain the detailed process of conducting IWRM modeling, including the role of contextual factors in shaping practices. We draw on findings from literature and the authors’ collective experience to articulate what and how contextual factors play out in employing those practices. In order to accelerate our learning about how to improve IWRM modeling, the paper concludes with five key areas for future practice-related research: knowledge sharing, overcoming data limitations, informed stakeholder involvement, social equity and uncertainty management. © 2019 Elsevier Lt

Deletion of a Malaria Invasion Gene Reduces Death and Anemia, in Model Hosts

Malaria parasites induce complex cellular and clinical phenotypes, including anemia, cerebral malaria and death in a wide range of mammalian hosts. Host genes and parasite ‘toxins’ have been implicated in malarial disease, but the contribution of parasite genes remains to be fully defined. Here we assess disease in BALB/c mice and Wistar rats infected by the rodent malaria parasite Plasmodium berghei with a gene knock out for merozoite surface protein (MSP) 7. MSP7 is not essential for infection but in P. falciparum, it enhances erythrocyte invasion by 20%. In vivo, as compared to wild type, the P. berghei Δmsp7 mutant is associated with an abrogation of death and a decrease from 3% to 2% in peak, circulating parasitemia. The Δmsp7 mutant is also associated with less anemia and modest increase in the size of follicles in the spleen. Together these data show that deletion of a single parasite invasion ligand modulates blood stage disease, as measured by death and anemia. This work is the first to assess the contribution of a gene present in all plasmodial species in severe disease