Time filter

Source Type

Park S.E.,CSIRO | Marshall N.A.,CSIRO | Jakku E.,CSIRO | Dowd A.M.,CSIRO | And 3 more authors.
Global Environmental Change | Year: 2012

Transformative actions are increasingly being required to address changes in climate. As an aid to understanding and supporting informed decision-making regarding transformative change, we draw on theories from both the resilience and vulnerability literature to produce the Adaptation Action Cycles concept and applied framework. The resulting Adaptation Action Cycles provides a novel conceptualisation of incremental and transformative adaptation as a continuous process depicted by two concentric and distinct, yet linked, action learning cycles. Each cycle represents four stages in the decision-making process, which are considered to be undertaken over relatively short timeframes. The concept is translated into an applied framework by adopting a contextual, actor-focused suite of questions at each of the four stages. This approach compliments existing theories of transition and transformation by operationalising assessments at the individual and enterprise level. Empirical validation of the concept was conducted by collaborating with members of the Australian wine industry to assess their decisions and actions taken in response to climate change. The contiguous stages represented in the Adaptation Action Cycles aptly reflected the diverse range of decision-making and action pathways taken in recent years by those interviewed. Results suggest that incremental adaptation decision-making processes have distinct characteristics, compared with those used in transformative adaptation. We provide empirical data to support past propositions suggesting dependent relationships operate between incremental and transformative scales of adaptation. © 2011 Elsevier Ltd. Source

Dausman A.M.,U.S. Geological Survey | Doherty J.,National Center for Groundwater Research and Training | Langevin C.D.,U.S. Geological Survey | Sukop M.C.,Florida International University
Ground Water | Year: 2010

The present study demonstrates a methodology for optimization of environmental data acquisition. Based on the premise that the worth of data increases in proportion to its ability to reduce the uncertainty of key model predictions, the methodology can be used to compare the worth of different data types, gathered at different locations within study areas of arbitrary complexity. The method is applied to a hypothetical nonlinear, variable density numerical model of salt and heat transport. The relative utilities of temperature and concentration measurements at different locations within the model domain are assessed in terms of their ability to reduce the uncertainty associated with predictions of movement of the salt water interface in response to a decrease in fresh water recharge. In order to test the sensitivity of the method to nonlinear model behavior, analyses were repeated for multiple realizations of system properties. Rankings of observation worth were similar for all realizations, indicating robust performance of the methodology when employed in conjunction with a highly nonlinear model. The analysis showed that while concentration and temperature measurements can both aid in the prediction of interface movement, concentration measurements, especially when taken in proximity to the interface at locations where the interface is expected to move, are of greater worth than temperature measurements. Nevertheless, it was also demonstrated that pairs of temperature measurements, taken in strategic locations with respect to the interface, can also lead to more precise predictions of interface movement. Journal compilation © 2010 National Ground Water Association. Source

Yang Y.,Tsinghua University | Shang S.,Tsinghua University | Guan H.,Flinders University | Guan H.,National Center for Groundwater Research and Training | Jiang L.,Tsinghua University
Journal of Geophysical Research: Biogeosciences | Year: 2013

Quantifying carbon fluxes at large spatial scales has attracted considerable scientific attentions. In this study, a novel approach was proposed to estimate the terrestrial ecosystem gross primary production (GPP) using imagery from the satellite-borne Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The new model (named Temperature and Greenness Rectangle, TGR) uses a combination of MODIS Enhanced Vegetation Index and Land Surface Temperature products as well as in situ measurement of photosynthetically active radiation to estimate GPP at a 16 day interval. Three major advantages are included in the model: (1) the model follows strictly the logic of the light use efficiency model and each parameter has physical meaning; (2) the model reduces the dependency on ground-based meteorological measurements; and (3) the overlap of information in correlated explanatory variables is avoided. The model was calibrated with data from 17 sites within the Ameriflux network and validated at another 13 sites, covering a wide range of climates and eight major vegetation types. Results show that the TGR model explains reasonably well the tower-based measurements of GPP for all vegetation types, except for the evergreen broadleaf forest, with the coefficient of determination in a range from 0.67 to 0.91 and the root mean square error from 9.0 to 31.9 g C/m2/16 days. Comparisons with other two models (the TG and GR model) show that the TGR model generally gives better GPP estimates in nearly all vegetation types, especially under dry climate conditions. These results indicate that the TGR model can be potentially used to estimate GPP at regional scale. © 2013. American Geophysical Union. All Rights Reserved. Source

Skurray J.H.,University of Western Australia | Skurray J.H.,National Center for Groundwater Research and Training | Roberts E.J.,GHD Pty Ltd | Roberts E.J.,Edith Cowan University | And 2 more authors.
Journal of Hydrology | Year: 2012

Perth, Western Australia (pop. 1.6m) derives 60% of its public water supply from the Gnangara groundwater system (GGS). Horticulture, domestic self-supply, and municipal parks are other major consumers of GGS groundwater. The system supports important wetlands and groundwater-dependent ecosystems. Underlying approximately 2200km 2 of the Swan Coastal Plain, the GGS comprises several aquifer levels with partial interconnectivity. Supplies of GGS groundwater are under unprecedented stress, due to reduced recharge and increases in extraction. Stored reserves in the superficial aquifer fell by 700GL between 1979 and 2008. Over a similar period, annual extraction for public supply increased by more than 350% from the system overall. Some management areas are over-allocated by as much as 69%.One potential policy response is a trading scheme for groundwater use. There has been only limited trading between GGS irrigators. Design and implementation of a robust groundwater trading scheme faces hydrological and/or hydro-economic challenges, among others. Groundwater trading involves transfers of the right to extract water. The resulting potential for spatial (and temporal) redistribution of the impacts of extraction requires management. Impacts at the respective selling and buying locations may differ in scale and nature. Negative externalities from groundwater trading may be uncertain as well as not monetarily compensable.An ideal groundwater trading scheme would ensure that marginal costs from trades do not exceed marginal benefits, incorporating future effects and impacts on third-parties. If this condition could be met, all transactions would result in constant or improved overall welfare. This paper examines issues that could reduce public welfare if groundwater trading is not subject to well-designed governance arrangements that are appropriate to meeting the above condition. It also outlines some opportunities to address key risks within the design of a groundwater trading scheme. We present a number of challenges, focusing on those with hydrological bases and/or information requirements. These include the appropriate hydrological definition of the boundaries of a trading area, the establishment and defining of sustainable yield and consumptive pool, and the estimation of effects of extractions on ecosystems and human users. We suggest several possible design tools. A combination of sustainable extraction limits, trading rules, management areas, and/or exchange rates may enable a trading scheme to address the above goals. © 2011 Elsevier B.V. Source

Perez C.,University of Chile | Mariethoz G.,University of New South Wales | Mariethoz G.,National Center for Groundwater Research and Training | Ortiz J.M.,University of Chile
Computers and Geosciences | Year: 2014

Parameter inference is a key aspect of spatial modeling. A major appeal of variograms is that they allow inferring the spatial structure solely based on conditioning data. This is very convenient when the modeler does not have a ready-made geological interpretation. To date, such an easy and automated interpretation is not available in the context of most multiple-point geostatistics applications. Because training images are generally conceptual models, their preparation is often based on subjective criteria of the modeling expert. As a consequence, selection of an appropriate training image is one of the main issues one must face when using multiple-point simulation. This paper addresses the development of a geostatistical tool that addresses two separate problems. It allows (1) ranking training images according to their relative compatibility to the data, and (2) obtaining an absolute measure quantifying the consistency between training image and data in terms of spatial structure. For both, two alternative implementations are developed. The first one computes the frequency of each pattern in each training image. This method is statistically sound but computationally demanding. The second implementation obtains similar results at a lesser computational cost using a direct sampling approach. The applicability of the methodologies is successfully evaluated in two synthetic 2D examples and one real 3D mining example at the Escondida Norte deposit. © 2014 Elsevier Ltd. Source

Discover hidden collaborations