Measuring scarce water saving from interregional virtual water flows in China

Trade of commodities can lead to virtual water flows between trading partners. When commodities flow from regions of high water productivity to regions of low water productivity, the trade has the potential to generate water saving. However, this accounting of water saving does not account for the water scarcity status in different regions. It could be that the water saving generated from this trade occurs at the expense of the intensified water scarcity in the exporting region, and exerts limited effect on water stress alleviation in importing regions. In this paper, we propose an approach to measure the scarce water saving associated with virtual water trade (measuring in water withdrawal/use). The scarce water is quantified by multiplying the water use in production with the water stress index. We assessed the scarce water saving/loss through interprovincial trade within China using a multi-region input-output table from 2010. The results show that interprovincial trade resulted in 14.2 km3 of water loss without considering water stress, but only 0.4 km3 scarce water loss using the scarce water concept. Among the 435 total connections of virtual water flows, 254 connections contributed to 20.2 km3 of scarce water saving. Most of these connections are virtual water flows from provinces with lower water stress index (WSI) to that with higher both water scarcity status and water productivity across regions. Identifying key connections of scarce water saving is useful in guiding interregional economic restructuring towards water stress alleviation, a major goal of China’s sustainable development strategy

Local Difference Measures between Complex Networks for Dynamical System Model Evaluation

Acknowledgments We thank Reik V. Donner for inspiring suggestions that initialized the work presented herein. Jan H. Feldhoff is credited for providing us with the STARS simulation data and for his contributions to fruitful discussions. Comments by the anonymous reviewers are gratefully acknowledged as they led to substantial improvements of the manuscript.Peer reviewedPublisher PD

Bioenergy production and sustainable development: science base for policymaking remains limited

The possibility of using bioenergy as a climate change mitigation measure has sparked a discussion of whether and how bioenergy production contributes to sustainable development. We undertook a systematic review of the scientific literature to illuminate this relationship and found a limited scientific basis for policymaking. Our results indicate that knowledge on the sustainable development impacts of bioenergy production is concentrated in a few well-studied countries, focuses on environmental and economic impacts, and mostly relates to dedicated agricultural biomass plantations. The scope and methodological approaches in studies differ widely and only a small share of the studies sufficiently reports on context and/or baseline conditions, which makes it difficult to get a general understanding of the attribution of impacts. Nevertheless, we identified regional patterns of positive or negative impacts for all categories – environmental, economic, institutional, social and technological. In general, economic and technological impacts were more frequently reported as positive, while social and environmental impacts were more frequently reported as negative (with the exception of impacts on direct substitution of GHG emission from fossil fuel). More focused and transparent research is needed to validate these patterns and develop a strong science underpinning for establishing policies and governance agreements that prevent/mitigate negative and promote positive impacts from bioenergy production

Towards a fair comparison of statistical and dynamical downscaling in the framework of the EURO-CORDEX initiative

Both statistical and dynamical downscaling methods are well established techniques to bridge the gap between the coarse information produced by global circulation models and the regional-to-local scales required by the climate change Impacts, Adaptation, and Vulnerability (IAV) communities. A number of studies have analyzed the relative merits of each technique by inter-comparing their performance in reproducing the observed climate, as given by a number of climatic indices (e.g. mean values, percentiles, spells). However, in this paper we stress that fair comparisons should be based on indices that are not affected by the calibration towards the observed climate used for some of the methods. We focus on precipitation (over continental Spain) and consider the output of eight Regional Climate Models (RCMs) from the EURO-CORDEX initiative at 0.44? resolution and five Statistical Downscaling Methods (SDMs) ?analog resampling, weather typing and generalized linear models? trained using the Spain044 observational gridded dataset on exactly the same RCM grid. The performance of these models is inter-compared in terms of several standard indices ?mean precipitation, 90th percentile on wet days, maximum precipitation amount and maximum number of consecutive dry days? taking into account the parameters involved in the SDM training phase. It is shown, that not only the directly affected indices should be carefully analyzed, but also those indirectly influenced (e.g. percentile-based indices for precipitation) which are more difficult to identify. We also analyze how simple transformations (e.g. linear scaling) could be applied to the outputs of the uncalibrated methods in order to put SDMs and RCMs on equal footing, and thus perform a fairer comparison.We acknowledge the World Climate Research Programme’s Working Group on Regional Climate, and theWorking Group on CoupledModelling, former coordinating body of CORDEX and responsible panel for CMIP5. We also thank the climate modeling groups (listed in Table 1 of this paper) for producing and making available their model output. We also acknowledge the Earth System Grid Federation infrastructure and AEMET and University of Cantabria for the Spain02 dataset (available at http: //www.meteo.unican.es/en/datasets/spain02). All the statistical downscaling experiments have been computed using theMeteoLab software (http://www.meteo.unican.es/software/meteolab), which is an open-source Matlab toolbox for statistical downscaling. This work has been partially supported by CORWES (CGL2010-22158-C02) and EXTREMBLES (CGL2010-21869) projects funded by the Spanish R&D programme. AC thanks the Spanish Ministry of Economy and Competitiveness for the funding provided within the FPI programme (BES-2011-047612 and EEBB-I-13-06354), JMG acknowledges the support from the SPECS project (FP7-ENV-2012-308378) and JF is grateful to the EUPORIAS project (FP7-ENV-2012-308291). We also thank three anonymous referees for their useful comments that helped to improve the original manuscript

Complex networks for climate model evaluation with application to statistical versus dynamical modeling of South American climate

Acknowledgments: This paper was developed within the scope of the IRTG 1740/TRP 2011/50151-0, funded by the DFG/FAPESP. Furthermore, this work has been financially supported by the Leibniz Society (project ECONS), and the Stordalen Foundation (JFD). For certain calculations, the software packages pyunicorn (Donges et al. 2013a) and igraph (Csa´rdi and Nepusz 2006) were used. The authors would like to thank Manoel F. Cardoso, Niklas Boers, and the reviewers for helpful comments on the manuscript. Open Access: This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.Peer reviewedPostprin

Textile-reinforced mortar (TRM) versus fibre-reinforced polymers (FRP) in flexural strengthening of RC beams

The aim of this paper is to compare the flexural performance of reinforced concrete (RC) beams strengthened with textile-reinforced mortar (TRM) and fibre-reinforced polymers (FRP). The investigated parameters included the strengthening material, namely TRM or FRP; the number of TRM/FRP layers; the textile surface condition (coated and uncoated); the textile fibre material (carbon, coated basalt or glass fibres); and the end-anchorage system of the external reinforcement. Thirteen RC beams were fabricated, strengthened and tested in four-point bending. One beam served as control specimen, seven beams strengthened with TRM, and five with FRP. It was mainly found that: (a) TRM was generally inferior to FRP in enhancing the flexural capacity of RC beams, with the effectiveness ratio between the two systems varying from 0.46 to 0.80, depending on the parameters examined, (b) by tripling the number of TRM layers (from one to three), the TRM versus FRP effectiveness ratio was almost doubled, (c) providing coating to the dry textile enhanced the TRM effectiveness and altered the failure mode; (d) different textile materials, having approximately same axial stiffness, resulted in different flexural capacity increases; and (e) providing end-anchorage had a limited effect on the performance of TRM-retrofitted beams. Finally, a simple formula proposed by fib Model Code 2010 for FRP reinforcement was used to predict the mean debonding stress developed in the TRM reinforcement. It was found that this formula is in a good agreement with the average stress calculated based on the experimental results when failure was similar to FRP-strengthened beams

Active and passive-source seismic imaging for exploration of deep-seated massive sulphide mineralization in the Zinkgruvan mine, south-central Sweden

[EN]This communication presents the acquisition of active and passive source seismic data in the Zinkgruvan mine in Sweden in an effort to develop highly resolved and cost-effective exploration method

SIT4ME: seismic imaging for mining exploration in Sotiel-Elvira (Spain)

[EN]This presentation summarizes an effort to develop cost effective techniques for mining exploratio

Allowable CO2 emissions based on regional and impact-related climate targets

This paper was accepted for publication in the journal Nature and the definitive published version is available at http://dx.doi.org/10.1038/nature16542© 2016 Macmillan Publishers Limited. All rights reserved. Global temperature targets, such as the widely accepted limit of an increase above pre-industrial temperatures of two degrees Celsius, may fail to communicate the urgency of reducing carbon dioxide (CO2) emissions. The translation of CO2 emissions into regional- and impact-related climate targets could be more powerful because such targets are more directly aligned with individual national interests. We illustrate this approach using regional changes in extreme temperatures and precipitation. These scale robustly with global temperature across scenarios, and thus with cumulative CO2 emissions. This is particularly relevant for changes in regional extreme temperatures on land, which are much greater than changes in the associated global mean

Projections of global warming-induced impacts on winter storm losses in the German private household sector

We present projections of winter storm-induced insured losses in the German residential building sector for the 21st century. With this aim, two structurally most independent downscaling methods and one hybrid downscaling method are applied to a 3-member ensemble of ECHAM5/MPI-OM1 A1B scenario simulations. One method uses dynamical downscaling of intense winter storm events in the global model, and a transfer function to relate regional wind speeds to losses. The second method is based on a reshuffling of present day weather situations and sequences taking into account the change of their frequencies according to the linear temperature trends of the global runs. The third method uses statistical-dynamical downscaling, considering frequency changes of the occurrence of storm-prone weather patterns, and translation into loss by using empirical statistical distributions. The A1B scenario ensemble was downscaled by all three methods until 2070, and by the (statistical-) dynamical methods until 2100. Furthermore, all methods assume a constant statistical relationship between meteorology and insured losses and no developments other than climate change, such as in constructions or claims management. The study utilizes data provided by the German Insurance Association encompassing 24 years and with district-scale resolution. Compared to 1971–2000, the downscaling methods indicate an increase of 10-year return values (i.e. loss ratios per return period) of 6–35 % for 2011–2040, of 20–30 % for 2041–2070, and of 40–55 % for 2071–2100, respectively. Convolving various sources of uncertainty in one confidence statement (data-, loss model-, storm realization-, and Pareto fit-uncertainty), the return-level confidence interval for a return period of 15 years expands by more than a factor of two. Finally, we suggest how practitioners can deal with alternative scenarios or possible natural excursions of observed losses