Commonwealth Scientific and Industrial Research Organisation

About: Commonwealth Scientific and Industrial Research Organisation is a government organization based out in Canberra, Australian Capital Territory, Australia. It is known for research contribution in the topics: Population & Soil water. The organization has 33765 authors who have published 79910 publications receiving 3356114 citations.

Governing Water: Contentious Transnational Politics and Global Institution Building

Abstract: Water is a key component of critical ecosystems, a marketable commodity, a foundation of local communities and cultures, and a powerful means of social control. It has become a source of contentious politics and social controversy on a global scale, and the management of water conflicts is one of the biggest challenges in the effort to achieve effective global environmental governance.In Governing Water, Ken Conca examines political struggles to create a global framework for the governance of water. Threats to the world's rivers, watersheds, and critical freshwater ecosystems have resisted the establishment of effective global agreements through intergovernmental bargaining because the conditions for successful interstate cooperation -- effective state authority, stable knowledge frameworks, and a territorialized understanding of nature -- cannot be imposed upon water controversies. But while interstate water diplomacy has faltered, less formalized institutions -- socially and politically embedded rules, roles, and practices -- have emerged to help shape water governance locally and globally.Conca examines the politics of these institutions, presenting a framework for understanding global environmental governance based on key institutional presumptions about territoriality, authority, and knowledge. He maps four distinct processes of institution building: formal international regimes for shared rivers; international networking among water experts and professionals; social movements opposing the construction of large dams; and the struggle surrounding transnational water "marketization." These cases illustrate the potential for alternative institutional forms in situations where traditional interstate regimes are ineffective.

Multi-decadal trends in global terrestrial evapotranspiration and its components

Abstract: Evapotranspiration (ET) is the process by which liquid water becomes water vapor and energetically this accounts for much of incoming solar radiation. If this ET did not occur temperatures would be higher, so understanding ET trends is crucial to predict future temperatures. Recent studies have reported prolonged declines in ET in recent decades, although these declines may relate to climate variability. Here, we used a well-validated diagnostic model to estimate daily ET during 1981-2012, and its three components: transpiration from vegetation (Et), direct evaporation from the soil (Es) and vaporization of intercepted rainfall from vegetation (Ei). During this period, ET over land has increased significantly (p < 0.01), caused by increases in Et and Ei, which are partially counteracted by Es decreasing. These contrasting trends are primarily driven by increases in vegetation leaf area index, dominated by greening. The overall increase in Et over land is about twofold of the decrease in Es. These opposing trends are not simulated by most Coupled Model Intercomparison Project phase 5 (CMIP5) models, and highlight the importance of realistically representing vegetation changes in earth system models for predicting future changes in the energy and water cycle.

Weather conditions conducive to Beijing severe haze more frequent under climate change

Abstract: Severe winter air pollution events, attributed to emissions from development, have increased in Beijing in recent decades. This study looks at how atmospheric conditions contribute and projects climate change will increase conditions favourable to such events. The frequency of Beijing winter severe haze episodes has increased substantially over the past decades1,2,3,4, and is commonly attributed to increased pollutant emissions from China’s rapid economic development5,6. During such episodes, levels of fine particulate matter are harmful to human health and the environment, and cause massive disruption to economic activities3,4,7,8,9,10,11,12,13,14,15,16, as occurred in January 201317,18,19,20,21. Conducive weather conditions are an important ingredient of severe haze episodes3,21, and include reduced surface winter northerlies3,21, weakened northwesterlies in the midtroposphere, and enhanced thermal stability of the lower atmosphere1,3,16,21. How such weather conditions may respond to climate change is not clear. Here we project a 50% increase in the frequency and an 80% increase in the persistence of conducive weather conditions similar to those in January 2013, in response to climate change. The frequency and persistence between the historical (1950–1999) and future (2050–2099) climate were compared in 15 models under Representative Concentration Pathway 8.5 (RCP8.5)22. The increased frequency is consistent with large-scale circulation changes, including an Arctic Oscillation upward trend23,24, weakening East Asian winter monsoon25,26, and faster warming in the lower troposphere27,28. Thus, circulation changes induced by global greenhouse gas emissions can contribute to the increased Beijing severe haze frequency.

ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data

Abstract: Scene understanding of high resolution aerial images is of great importance for the task of automated monitoring in various remote sensing applications. Due to the large within-class and small between-class variance in pixel values of objects of interest, this remains a challenging task. In recent years, deep convolutional neural networks have started being used in remote sensing applications and demonstrate state of the art performance for pixel level classification of objects. Here we propose a reliable framework for performant results for the task of semantic segmentation of monotemporal very high resolution aerial images. Our framework consists of a novel deep learning architecture, ResUNet-a , and a novel loss function based on the Dice loss. ResUNet-a uses a UNet encoder/decoder backbone, in combination with residual connections, atrous convolutions, pyramid scene parsing pooling and multi-tasking inference. ResUNet-a infers sequentially the boundary of the objects, the distance transform of the segmentation mask, the segmentation mask and a colored reconstruction of the input. Each of the tasks is conditioned on the inference of the previous ones, thus establishing a conditioned relationship between the various tasks, as this is described through the architecture’s computation graph. We analyse the performance of several flavours of the Generalized Dice loss for semantic segmentation, and we introduce a novel variant loss function for semantic segmentation of objects that has excellent convergence properties and behaves well even under the presence of highly imbalanced classes. The performance of our modeling framework is evaluated on the ISPRS 2D Potsdam dataset. Results show state-of-the-art performance with an average F1 score of 92.9% over all classes for our best model.

Height-diameter allometry of tropical forest trees

Abstract: . Tropical tree height-diameter (H:D) relationships may vary by forest type and region making large-scale estimates of above-ground biomass subject to bias if they ignore these differences in stem allometry. We have therefore developed a new global tropical forest database consisting of 39 955 concurrent H and D measurements encompassing 283 sites in 22 tropical countries. Utilising this database, our objectives were: 1. to determine if H:D relationships differ by geographic region and forest type (wet to dry forests, including zones of tension where forest and savanna overlap). 2. to ascertain if the H:D relationship is modulated by climate and/or forest structural characteristics (e.g. stand-level basal area, A). 3. to develop H:D allometric equations and evaluate biases to reduce error in future local-to-global estimates of tropical forest biomass. Annual precipitation coefficient of variation (PV), dry season length (SD), and mean annual air temperature (TA) emerged as key drivers of variation in H:D relationships at the pantropical and region scales. Vegetation structure also played a role with trees in forests of a high A being, on average, taller at any given D. After the effects of environment and forest structure are taken into account, two main regional groups can be identified. Forests in Asia, Africa and the Guyana Shield all have, on average, similar H:D relationships, but with trees in the forests of much of the Amazon Basin and tropical Australia typically being shorter at any given D than their counterparts elsewhere. The region-environment-structure model with the lowest Akaike's information criterion and lowest deviation estimated stand-level H across all plots to within amedian −2.7 to 0.9% of the true value. Some of the plot-to-plot variability in H:D relationships not accounted for by this model could be attributed to variations in soil physical conditions. Other things being equal, trees tend to be more slender in the absence of soil physical constraints, especially at smaller D. Pantropical and continental-level models provided less robust estimates of H, especially when the roles of climate and stand structure in modulating H:D allometry were not simultaneously taken into account.