This review and commentary sets out the need for authoritative and concise information on the expected error distributions and magnitudes in observational data. We discuss the necessary components of a benchmark of dominant data uncertainties and the recent developments in hydrology which increase the need for such guidance. We initiate the creation of a catalogue of accessible information on characteristics of data uncertainty for the key hydrological variables of rainfall, river discharge and water quality (suspended solids, phosphorus and nitrogen). This includes demonstration of how uncertainties can be quantified, summarizing current knowledge and the standard quantitative results available. In particular, synthesis of results from multiple studies allows conclusions to be drawn on factors which control the magnitude of data uncertainty and hence improves provision of prior guidance on those uncertainties. Rainfall uncertainties were found to be driven by spatial scale, whereas river discharge uncertainty was dominated by flow condition and gauging method. Water quality variables presented a more complex picture with many component errors. For all variables, it was easy to find examples where relative error magnitudes exceeded 40%. We consider how data uncertainties impact on the interpretation of catchment dynamics, model regionalization and model evaluation. In closing the review, we make recommendations for future research priorities in quantifying data uncertainty and highlight the need for an improved ‘culture of engagement’ with observational uncertainties. Copyright © 2012 John Wiley & Sons, Ltd.

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10.1002/hyp.7587.abs In order to quantify total error affecting hydrological models and predictions, we must explicitly recognize errors in input data, model structure, model parameters and validation data. This paper tackles the last of these: errors in discharge measurements used to calibrate a rainfall-runoff model, caused by stage–discharge rating-curve uncertainty. This uncertainty may be due to several combined sources, including errors in stage and velocity measurements during individual gaugings, assumptions regarding a particular form of stage–discharge relationship, extrapolation of the stage–discharge relationship beyond the maximum gauging, and cross-section change due to vegetation growth and/or bed movement. A methodology is presented to systematically assess and quantify the uncertainty in discharge measurements due to all of these sources. For a given stage measurement, a complete PDF of true discharge is estimated. Consequently, new model calibration techniques can be introduced to explicitly account for the discharge error distribution. The method is demonstrated for a gravel-bed river in New Zealand, where all the above uncertainty sources can be identified, including significant uncertainty in cross-section form due to scour and re-deposition of sediment. Results show that rigorous consideration of uncertainty in flow data results in significant improvement of the model's ability to predict the observed flow. Copyright © 2010 John Wiley & Sons, Ltd

Journal article;CRP5; ISI; Southern Africa‘s hydro-economy and water security (SAHEWS)EPTD; DSGDPRCGIAR Research Program on Water, Land and Ecosystems (WLE

[1] It is demonstrated for the first time how model parameter, structural and data uncertainties can be accounted for explicitly and simultaneously within the Generalized Likelihood Uncertainty Estimation (GLUE) methodology. As an example application, 72 variants of a single soil moisture accounting store are tested as simplified hypotheses of runoff generation at six experimental grassland field-scale lysimeters through model rejection and a novel diagnostic scheme. The fields, designed as replicates, exhibit different hydrological behaviors which yield different model performances. For fields with low initial discharge levels at the beginning of events, the conceptual stores considered reach their limit of applicability. Conversely, one of the fields yielding more discharge than the others, but having larger data gaps, allows for greater flexibility in the choice of model structures. As a model learning exercise, the study points to a "leaking" of the fields not evident from previous field experiments. It is discussed how understanding observational uncertainties and incorporating these into model diagnostics can help appreciate the scale of model structural error.

Water research is introduced from the combined perspectives of natural and social science and cases of citizen and stakeholder coproduction of knowledge. Using the overarching notion of transdisciplinarity, we examine how interdisciplinary and participatory water research has taken place and could be developed further. It becomes apparent that water knowledge is produced widely within society, across certified disciplinary experts and noncertified expert stakeholders and citizens. However, understanding and management interventions may remain partial, or even conflicting, as much research across and between traditional disciplines has failed to integrate disciplinary paradigms due to philosophical, methodological, and communication barriers. We argue for more agonistic relationships that challenge both certified and noncertified knowledge productively. These should include examination of how water research itself embeds and is embedded in social context and performs political work. While case studies of the cultural and political economy of water knowledge exist, we need more empirical evidence on how exactly culture, politics, and economics have shaped this knowledge and how and at what junctures this could have turned out differently. We may thus channel the coproductionist critique productively to bring perspectives, alternative knowledges, and implications into water politics where they were not previously considered; in an attempt to counter potential lock‐in to particular water policies and technologies that may be inequitable, unsustainable, or unacceptable. While engaging explicitly with politics, transdisciplinary water research should remain attentive to closing down moments in the research process, such as framings, path‐dependencies, vested interests, researchers’ positionalities, power, and scale. WIREs Water 2016, 3:369–389. doi: 10.1002/wat2.1132For further resources related to this article, please visit the WIREs website.

Global municipal waste production causes multiple environmental impacts, including greenhouse gas emissions, ocean plastic accumulation, and nitrogen pollution. However, estimates of both past and future development of waste and pollution are scarce. We apply compositional Bayesian regression to produce the first estimates of past and future (1965–2100) waste generation disaggregated by composition and treatment, along with resultant environmental impacts, for every country. We find that total wastes grow at declining speed with economic development, and that global waste generation has increased from 635 Mt in 1965 to 1999 Mt in 2015 and reaches 3539 Mt by 2050 (median values, middle-of-the-road scenario). From 2015 to 2050, the global share of organic waste declines from 47% to 39%, while all other waste type shares increase, especially paper. The share of waste treated in dumps declines from 28% to 18%, and more sustainable recycling, composting, and energy recovery treatments increase. Despite these increases, we estimate environmental loads to continue increasing in the future, although yearly plastic waste input into the oceans has reached a peak. Waste production does not appear to follow the environmental Kuznets curve, and current projections do not meet UN SDGs for waste reduction. Our study shows that a continuation of current trends and improvements is insufficient to reduce pressures on natural systems and achieve a circular economy. Relative to 2015, the amount of recycled waste would need to increase from 363 Mt to 740 Mt by 2030 to begin reducing unsustainable waste generation, compared to 519 Mt currently projected.

(2016) Reductionist and integrative research approaches to complex water security policy challenges. Global Environmental Change, 39 . pp. 143-154. ISSN 0959-3780 DOI: 10.1016/j.gloenvcha.2016 Reuse of this item is permitted through licensing under the Creative Commons: AbstractThis article reviews and contrasts two approaches that water security researchers employ to advance understanding of the complexity of water-society policy challenges. A prevailing reductionist approach seeks to represent uncertainty through calculable risk, links national GDP tightly to hydro-climatological causes, and underplays diversity and politics in society. When adopted uncritically, this approach limits policy-makers to interventions that may reproduce inequalities, and that are too rigid to deal with future changes in society and climate. A second, more integrative, approach is found to address a range of uncertainties, explicitly recognise diversity in society and the environment, incorporate water resources that are less-easily controlled, and consider adaptive approaches to move beyond conventional supply-side prescriptions. The resultant policy recommendations are diverse, inclusive, and more likely to reach the marginalised in society, though they often encounter policy-uptake obstacles. The article concludes by defining a route towards more effective water security research and policy, which stresses analysis that matches the state of knowledge possessed, an expanded research agenda, and explicitly addresses inequities.Complex Water Security