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As evidenced by the EU Floods Directive (2007/60/EC), flood management strategies in Europe have undergone a shift in focus in recent years. The goal of flood prevention using structural measures has been replaced by an emphasis on the management of flood risks using non-structural measures. One implication of this is that public authorities alone not only take responsibility for flood management. A broader range of stakeholders, who may personally experience the negative effects of flooding, also take on responsibility for protecting themselves. Therefore, it is vital that information concerning flood risks is conveyed to those who may be affected in order to facilitate the self-protection of citizens. Experience shows that problems persist even where efforts have been made to communicate flood risks. There is a need for the development of new tools that are able to rapidly disseminate flood-risk information to the general public. To be useful these tools must be able to present information relevant to the location of the user. Moreover, the content and design of the tool need to be adjusted to laypeople's needs. Dissemination and communication influence both people's access to and understanding of natural risk information. Such a tool could be a useful aid to effective management of flood risks. To address this gap, a web-based geographical information system (WebGIS) has been developed through the collaborative efforts of a group of scientists, hazard and risk analysts and managers, GIS analysts, system developers and communication designers. This tool, called "READY: Risk, Extreme Events, Adaptation, Defend Yourself", aims to enhance the general public knowledge of flood risk, making citizens more capable of responding appropriately during a flood event. The READY WebGIS has allowed for the visualization and easy querying of a complex hazard and risk database thanks to a high degree of interactivity and easily read maps. In this way, READY has enabled fast exploration of alternative flood scenarios or past calamitous events. Combined also with a system of graphic symbols designed ad hoc for communication of self-protection behaviours, it is believed READY could lead to an increase in citizen participation, informed discussion and consensus building. The platform has been developed for a site-specific application: the Basilicata region, Italy, has been selected as pilot application area. The goal of the prototype is to raise citizen awareness of flood risks and to build social capacity and enhanced resilience to flood events.

Efficient decision-making regarding flood risk reduction has become a priority for authorities and stakeholders in many European countries. Risk analysis methods and techniques are a useful tool for evaluating costs and benefits of possible interventions. Within this context, a methodology to estimate flood consequences was developed in this paper that is based on GIS, and integrated with a model that estimates the degree of accessibility and operability of strategic emergency response structures in an urban area. The majority of the currently available approaches do not properly analyse road network connections and dependencies within systems, and as such a loss of roads could cause significant damages and problems to emergency services in cases of flooding. The proposed model is unique in that it provides a maximum-impact estimation of flood consequences on the basis of the operability of the strategic emergency structures in an urban area, their accessibility, and connection within the urban system of a city (i.e. connection between aid centres and buildings at risk), in the emergency phase. The results of a case study in the Puglia region in southern Italy are described to illustrate the practical applications of this newly proposed approach. The main advantage of the proposed approach is that it allows for defining a hierarchy between different infrastructure in the urban area through the identification of particular components whose operation and efficiency are critical for emergency management. This information can be used by decision-makers to prioritize risk reduction interventions in flood emergencies in urban areas, given limited financial resources.

Applying a collaborative approach under a power-sharing institutional structure, coupled with a shift in paradigms, sustainable water resources management often requires political-institutional reform to achieve its goals. Most of Brazil’s river basins are subject to rapid urbanization; however, basin stakeholders generally lack sufficient institutional capacity to address the attending water resource issues. Subject to urbanisation, the Pitimbu River basin supplies potable water to approximately 280 000 people in Brazil’s Natal region. This study investigated how current institutional models influence both water management and fluvial contamination by metals. Sediment samples collected at eight sites along the river revealed elevated levels of Pb, Fe, Al, Ni and Zn, whose sources were linked to industries, vehicles, as well as agricultural and construction wastes. Aluminium enrichment of surface waters was mainly linked to inadequate sanitation infrastructure. In light of this, the region's poor institutional capacity must be addressed through institutional reform, including a new management structure open to public collective water management planning. In so doing, Brazil's water policies should acknowledge capacity building as a critical element of institutional reform.

In many situations the extension of hydrological or water quality time series at short-gauged stations is required. Ordinary least squares regression (OLS) of any hydrological or water quality variable is a traditional and commonly used record extension technique. However, OLS tends to underestimate the variance in the extended records, which leads to underestimation of high percentiles and overestimation of low percentiles, given that the data are normally distributed. The development of the line of organic correlation (LOC) technique is aimed at correcting this bias. On the other hand, the Kendall-Theil robust line (KTRL) method has been proposed as an analogue of OLS with the advantage of being robust in the presence of outliers. Given that water quality data are characterised by the presence of outliers, positive skewness and non-normal distribution of data, a robust record extension technique is more appropriate. In this paper, four record-extension techniques are described, and their properties are explored. These techniques are OLS, LOC, KTRL and a new technique proposed in this paper, the robust line of organic correlation technique (RLOC). RLOC includes the advantage of the LOC in reducing the bias in estimating the variance, but at the same time it is also robust in the presence of outliers. A Monte Carlo study and empirical experiment were conducted to examine the four techniques for the accuracy and precision of the estimate of statistical moments and over the full range of percentiles. Results of the Monte Carlo study showed that the OLS and KTRL techniques have serious deficiencies as record-extension techniques, while the LOC and RLOC techniques are nearly similar. However, RLOC outperforms OLS, KTRL and LOC when using real water quality records.

Drought forecasts can be an effective tool for mitigating some of the more adverse consequences of drought. Data-driven models are suitable forecasting tools due to their rapid development times, as well as minimal information requirements compared to the information required for physically based models. This study compares the effectiveness of three data-driven models for forecasting drought conditions in the Awash River Basin of Ethiopia. The Standard Precipitation Index (SPI) is forecast and compared using artificial neural networks (ANNs), support vector regression (SVR), and wavelet neural networks (WN). SPI 3 and SPI 12 were the SPI values that were forecasted. These SPI values were forecast over lead times of 1 and 6 months. The performance of all the models was compared using RMSE, MAE, and R2. The forecast results indicate that the coupled wavelet neural network (WN) models were the best models for forecasting SPI values over multiple lead times in the Awash River Basin in Ethiopia.

A proposal of an electrically controlled spin transistor in a helical magnetic field is presented. In the proposed device, the transistor action is driven by the Landau–Zener transitions that lead to a backscattering of spin polarized electrons and switching the transistor into the high-resistance state (off state). The on/off state of the transistor can be controlled by the all-electric means using Rashba spin–orbit coupling that can be tuned by the voltages applied to the side electrodes. [ABSTRACT FROM AUTHOR]

Spin-dependent electron transport has been studied in magnetic semiconductor waveguides (nanowires) in the helical magnetic field. We have shown that—apart from the well-known conductance dip located at the magnetic field equal to the helical-field amplitude Bh—the additional conductance dips (with zero conductance) appear at a magnetic field different from Bh. This effect occurring in the non-adiabatic regime is explained as resulting from the resonant Landau–Zener transitions between the spin-split subbands. [ABSTRACT FROM AUTHOR]

Identifying crop water footprints and their driving mechanisms is of significant importance for regional water resources management and ecological sustainability. However, there is currently a lack of comparative studies on drivers of crop water footprint among multiple regional types. In this study, based on quantifying the crop water footprints in seven regions (North China, Northeast China, East China, Central China, South China, Southwest China, and Northwest China) in mainland China from 1996 to 2020, the path analysis method was used to reveal their driving mechanisms. The results showed that the average annual agricultural water footprint was 1448.2 Gm 3 , with blue water, green water, and grey water accounting for 10.1 %, 66.6 %, and 23.3 %, respectively. Fruits and cereals jointly contributed 80 % of the total water footprint. The crop water footprint in East China was significantly higher than in other regions, accounting for 29.3 % of the national water footprint. The average crop production water footprint was 1080.4 mm, with the highest values observed in East China and South China, and the lowest in Northeast China and Southwest China. Except for East China, the crop production water footprint in other regions showed an increasing trend over time. Irrigation area ratio had the greatest impact on crop production water footprint except for Northeast China, while chemical fertilizer consumption significantly influenced crop production water footprints in North, East, Central, Southwest and Northwest China. Additionally, per capita GDP, per capita net income and irrigation water use efficiency also had considerable effects on crop production water footprint in Northwest China. The research findings can provide a valuable reference for the development of strategies for the efficient and sustainable utilization of agricultural water resources in different regions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Understanding the interactions between human and environmental systems is key to sustainable environmental management. Dynamically Coupled Socioeconomic system dynamics models integrated with physically-based Environmental Models (DCSEMs) are promising tools to appropriately capture the non-linear relationships between complex socioeconomic and biophysical systems, thereby supporting sustainable environmental management. However, existing approaches for testing integrated models are commonly based on the point-to-point analysis of model outputs, which is not suitable for DCSEMs that are behaviour pattern oriented. Consequently, the lack of well-defined behaviour pattern-based approaches has limited the adaptability of DCSEMs. To address this gap, this study proposes a novel behaviour pattern-based model testing approach that includes global sensitivity analysis (GSA), auto-calibration algorithms, and evaluation to assess behaviour pattern similarities between model outputs and real-world trends. The proposed approach is demonstrated through a real-world case study, in which an existing DCSEM is calibrated and evaluated to simulate water table depth in the Rechna Doab region of Pakistan. Compared to the conventional numerical point approach, the proposed approach is better suited for DCSEMs, as it replicates observed system behaviour patterns (as opposed to observed point values) over time. Furthermore, the outcomes of the Theil inequality statistical analysis and parameter distribution analysis provide evidence that the suggested approach is effective in testing and improving the performance of the DCSEM by capturing the spatial heterogeneity within the study area. The proposed behaviour-pattern testing procedure is a useful approach for model testing in data-limited, spatially-distributed DCSEMs., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Jan Adamowski reports financial support was provided by Government of Canada Social Sciences and Humanities Research Council., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Studies have identified elevation-dependent warming trends, but investigations of such trends in fire danger are absent in the literature. Here, we demonstrate that while there have been widespread increases in fire danger across the mountainous western US from 1979 to 2020, trends were most acute at high-elevation regions above 3000 m. The greatest increase in the number of days conducive to large fires occurred at 2500-3000 m, adding 63 critical fire danger days between 1979 and 2020. This includes 22 critical fire danger days occurring outside the warm season (May-September). Furthermore, our findings indicate increased elevational synchronization of fire danger in western US mountains, which can facilitate increased geographic opportunities for ignitions and fire spread that further complicate fire management operations. We hypothesize that several physical mechanisms underpinned the observed trends, including elevationally disparate impacts of earlier snowmelt, intensified land-atmosphere feedbacks, irrigation, and aerosols, in addition to widespread warming/drying., (© 2023. The Author(s).)

The assessment of climate change impacts requires downscaled climate projections and context-specific socioeconomic scenarios. The development of practical climate change adaptation for environmental sustainability at regional and local scales is predicated on a strong understanding of future socio-economic dynamics under a range of potential climate projections. We have addressed this need using integrated assessment of a localized hybrid Shared Socio-economic Pathway - Representative Concentration Pathway (SSP-RCP) framework, through an interdisciplinary and participatory storyline development process that integrates bottom-up local expert-stakeholder knowledge with top-down insights from global SSPs. We use the global SSPs (SSP1 to SSP5) as boundary conditions in conjunction with climate change pathways (RCP4.5, RCP8.5) to create localized SSP narratives in an iterative participatory process, using a storytelling method. By using an integrated socio-economic and environmental system dynamics model developed in collaboration with local stakeholders, we explore the potential impacts of plausible local SSP-RCP narratives and quantify important socio-environmental vulnerabilities of a human-water system (e.g., crop yields, farm income, water security and groundwater depletion) by the mid-century period (i.e., by 2050). The framework is developed to inform climate adaptation for Pakistan's Rechna Doab region, which serves as a representative case of a multi-stakeholder coupled human-water system operating in a developing country. Our results suggest that even under limited socio-economic improvements (e.g., technology, policies, institutions, environmental awareness) water security would be expected to decline and environmental degradation (e.g., groundwater depletion) to worsen. Under RCP 4.5, the average projected increase in water demand in 2030 will be about 7.32% for all SSP scenario narratives, and 10.82% by mid-century. Groundwater use varies significantly across SSPs which results in an average increase of about 29.06% for all SSPs. The proposed framework facilitates the development of future adaptation policies that should consider regional and local planning as well as socio-economic conditions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Despite perceptions of high water availability, adequate access to sufficient water resources remains a major challenge in Alaska. This paper uses a participatory modeling approach to investigate household water vulnerability in remote Alaska and to examine factors that affect water availability and water access. Specifically, the work asks: how do water policy stakeholders conceptualize the key processes that affect household water vulnerability in the context of rural Alaska? Fourteen water policy stakeholders participated in the modeling process, which included defining the problem of household water vulnerability and constructing individual causal loop diagrams (CLDs) that represent their conceptualization of household water vulnerability. Individual CLDs were subsequently combined and five sub-models emerged: environmental, economic, infrastructure, social, and health. The environmental and economic sub-models of the CLD are explored in depth. In the environmental sub-model, climate change and environmental barriers due to geography influence household water vulnerability. In the economic sub-model, four processes and one feedback loop affect household water vulnerability, including operations and maintenance funding, the strength of the rural Alaskan economy, and the impact of regulations. To overcome household water vulnerability and make households more resilient, stakeholders highlighted policy solutions under five themes: economics, social, regulatory, technological, and environmental.

Using over a century of ground-based observations over the contiguous United States, we show that the frequency of compound dry and hot extremes has increased substantially in the past decades, with an alarming increase in very rare dry-hot extremes. Our results indicate that the area affected by concurrent extremes has also increased significantly. Further, we explore homogeneity (i.e., connectedness) of dry-hot extremes across space. We show that dry-hot extremes have homogeneously enlarged over the past 122 years, pointing to spatial propagation of extreme dryness and heat and increased probability of continental-scale compound extremes. Last, we show an interesting shift between the main driver of dry-hot extremes over time. While meteorological drought was the main driver of dry-hot events in the 1930s, the observed warming trend has become the dominant driver in recent decades. Our results provide a deeper understanding of spatiotemporal variation of compound dry-hot extremes., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).)

While it remains the primary source of safe drinking and irrigation water in northwest Iran's Maku Plain, the region's groundwater is prone to fluoride contamination. Accordingly, modeling techniques to accurately predict groundwater fluoride concentration are required. The current paper advances several novel data mining algorithms including Lazy learners [instance-based K-nearest neighbors (IBK); locally weighted learning (LWL); and KStar], a tree-based algorithm (M5P), and a meta classifier algorithm [regression by discretization (RBD)] to predict groundwater fluoride concentration. Drawing on several groundwater quality variables (e.g., Ca 2 + , Mg 2 + , Na + , K + , HCO 3 - , CO 3 2 - , SO 4 2 - , and Cl - concentrations), measured in each of 143 samples collected between 2004 and 2008, several models predicting groundwater fluoride concentrations were developed. The full dataset was divided into two subsets: 70% for model training (calibration) and 30% for model evaluation (validation). Models were validated using several statistical evaluation criteria and three visual evaluation approaches (i.e., scatter plots, Taylor and Violin diagrams). Although Na + and Ca 2+ showed the greatest positive and negative correlations with fluoride (r = 0.59 and -0.39, respectively), they were insufficient to reliably predict fluoride levels; therefore, other water quality variables, including those weakly correlated with fluoride, should be considered as inputs for fluoride prediction. The IBK model outperformed other models in fluoride contamination prediction, followed by KStar, RBD, M5P, and LWL. The RBD and M5P models were the least accurate in terms of predicting peaks in fluoride concentration values. Results of the current study can be used to support practical and sustainable management of water and groundwater resources., (© 2019, National Ground Water Association.)

The DRASTIC technique is commonly used to assess groundwater vulnerability. The main disadvantage of the DRASTIC method is the difficulty associated with identifying appropriate ratings and weight assignments for each parameter. To mitigate this issue, ratings and weights can be approximated using different methods appropriate to the conditions of the study area. In this study, different linear (i.e., Wilcoxon test and statistical approaches) and nonlinear (Genetic algorithm [GA]) modifications for calibration of the DRASTIC framework using nitrate (NO 3 ) concentrations were compared through the preparation of groundwater vulnerability maps of the Meshqin-Shahr plain, Iran. Twenty-two groundwater samples were collected from wells in the study area, and their respective NO 3 concentrations were used to modify the ratings and weights of the DRASTIC parameters. The areas found to have the highest vulnerability were in the eastern, central, and western regions of the plain. Results showed that the modified DRASTIC frameworks performed well, compared to the unmodified DRASTIC. When measured NO 3 concentrations were correlated with the vulnerability indices produced by each method, the unmodified DRASTIC method performed most poorly, and the Wilcoxon-GA-DRASTIC method proved optimal. Compared to the unmodified DRASTIC method with an R 2 of 0.22, the Wilcoxon-GA-DRASTIC obtained a maximum R 2 value of 0.78. Modification of DRASTIC parameter ratings was found to be more efficient than the modification of the weights in establishing an accurately calibrated DRASTIC framework. However, modification of parameter ratings and weights together increased the R 2 value to the highest degree., (© 2019, National Ground Water Association.)

The objectives of this study were to measure some trace element concentrations in the groundwater of the Khoy area in northwestern Iran, understand their potential origins using multivariate statistical approaches (correlation analysis, cluster analysis and factor analysis), and evaluate their non-carcinogenic human health risks to local residents through drinking water intake. The trace element status of the groundwater and the associated health risks in the study area have not previously been reported. Groundwater water samples were collected from 54 water sources in July 2017 in the study area. Samples were measured for EC, pH, major and minor elements and some trace elements (Fe, Mn, Al, Zn, Cr, Pb, Cd, Co, Ni and As). The levels of EC, F, Cd, Pb, Zn, As and all the major ions except K exceeded permissible levels for drinking water. Multivariate analysis showed that the quality of groundwater was mainly controlled by geogenic factors followed by anthropogenic impacts. Health risk assessment results indicated that Cr and As in the groundwater, with hazard quotient values of 0.0001 and 11.55, respectively, had the lowest and highest impacts of non-carcinogenic risk to adults and children in the area. The high-risk samples were mainly situated in the northeast and southwest of the Khoy plain where the groundwater was saline. The health risk associated with water consumption from the unconfined aquifer was higher than that from the confined aquifer in the study area. Special attention should be paid to groundwater management in the high-risk areas to control factors (e.g., EC, pH and redox) that stimulate the release of trace elements into groundwater.

Atmospheric visibility (AV) is an indicator for assessing air quality and is measured in standard weather stations. The AV can change as a result of two main factors: air pollution and atmospheric humidity. This study aimed to investigate trends in the number of days with AV equal or less than 2 km (D AV2 ) in Iran during 1968-2013. Consequently, 43 weather stations with different climates were evaluated across the country, using the Mann-Kendall (MK) trend test. The results show that the number of stations with positive (i.e., significant or non-significant) MK z values was equal to, or greater than, those with negative MK z values, in all months and seasons of the year, as well as annually. Furthermore, summer and autumn had, respectively, the least and most stations with positive MK z values. Fewer trends in D AV2 were detected in the central, east, and northeast regions of the country. Analyzing the D AV2 and relative humidity together indicated that over 30% of stations had at-risk air quality in January, and that the largest number of stations with at-risk air quality was in the autumn and winter. These results are useful for better environmental planning to improve air quality, especially in developing countries such as Iran, where reduced air quality has been a major problem in recent decades.

Developing a reliable groundwater vulnerability and contamination risk map is very important for groundwater management and protection. This study aims to compare various modified DRASTIC vulnerability frameworks based on rate calibration using the Wilcoxon rank-sum test (WRST), frequency ratio (FR) and weight optimization using the correlation coefficient (CC), the analytic hierarchy process (AHP), and genetic algorithms (GA), as well as to introduce, for the first time, an aggregated approach based on a bagging ensemble to develop a combined modified DRASTIC model. This research was conducted in the Khoy plain, NW Iran. To develop a typical DRASTIC map, seven DRASTIC data layers were generated, weighted, and then overlaid in ArcGIS. The nitrate (NO 3 ) concentrations at 54 sites in the study area were used to validate the models by calculating the correlation coefficient (r) between the vulnerability/risk indices and NO 3 concentrations. The calculated r value for the typical DRASTIC was 0.12. A sensitivity analysis reveals that the impact of the vadose zone and conductivity parameters with mean variation indices of 22.2 and 7.5%, respectively, have the highest and lowest influence on aquifer vulnerability. The r values increased for all the optimized frameworks. The results show that the WRST and GA methods are the most effective methods for calibration and optimization of DRASTIC rates and weights, with the WRST-GA-DRASTIC model obtaining an r value of 0.64. A bagging ensemble model was employed to combine the advantages of each standalone model. The bagging ensemble model yields an r value of 0.67. The ensemble model has the potential to increase the r value further than both the standalone optimized frameworks and the typical DRASTIC approach. In terms of spatial distribution class area (%), the bagging ensemble-DRASTIC model demonstrates that the moderate and low contamination risk classes with 16.4 and 23.1% of the total area cover the lowest and highest parts of the plain.

Floods, as a catastrophic phenomenon, have a profound impact on ecosystems and human life. Modeling flood susceptibility in watersheds and reducing the damages caused by flooding is an important component of environmental and water management. The current study employs two new algorithms for the first time in flood susceptibility analysis, namely multivariate discriminant analysis (MDA), and classification and regression trees (CART), incorporated with a widely used algorithm, the support vector machine (SVM), to create a flood susceptibility map using an ensemble modeling approach. A flood susceptibility map was developed using these models along with a flood inventory map and flood conditioning factors (including altitude, slope, aspect, curvature, distance from river, topographic wetness index, drainage density, soil depth, soil hydrological groups, land use, and lithology). The case study area was the Khiyav-Chai watershed in Iran. To ensure a more accurate ensemble model, this study proposed a framework for flood susceptibility assessment where only those models with an accuracy of >80% were permissible for use in ensemble modeling. The relative importance of factors was determined using the Jackknife test. Results indicated that the MDA model had the highest predictive accuracy (89%), followed by the SVM (88%) and CART (0.83%) models. Sensitivity analysis showed that slope percent, drainage density, and distance from river were the most important factors in flood susceptibility mapping. The ensemble modeling approach indicated that residential areas at the outlet of the watershed were very susceptible to flooding, and that these areas should, therefore, be prioritized for the prevention and remediation of floods., (Copyright © 2018 Elsevier B.V. All rights reserved.)

The process of systematically developing a sustainability vision is an important element of effective environmental management. Sustainability visions can, however, include contradictions and counterintuitive effects due to complex system behavior (e.g., feedback loops, multi-causality) and ambiguous system boundaries (e.g., choice of a scale, such as a regional or national scale). This paper proposes an innovative methodological framework for vision design and assessment (VDA) to analyze the sustainability of future visions on multiple scales with consideration of ecosystem services, and to test their plausibility based upon expert and local knowledge. First, requirements and functions of visionary system designs are identified. Second, a functional organizational analysis defines structures and processes that generate functions. Third, a literature review and participatory modeling process are conducted to analyze the system structures of visionary system designs using causal loop diagrams. Fourth, fuzzy cognitive mapping is used to assess visions based upon sustainability indicators. A case study on sustainable food systems in Southwestern Ontario, Canada, is provided to demonstrate the application of the methodology. Three designs of a sustainable food system were analyzed and tested: urban organic gardening, a local diversified organic food system and a globalized commodity-based organic food system. The results show the advantages and disadvantages of each system design and underline the sustainability benefits of a multi-scale food system based upon a combination of system designs., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Novel methods for enhancing collaboration and interactions are required to ensure that stakeholders and governments are able to develop a shared vision that supports sustainable watershed governance. Particular attention must be placed on integrating stakeholders who would otherwise have limited decision-making power. By crossing professional, ideological and jurisdictional boundaries, stakeholders' perspectives are more likely to change than when staying within those boundaries. This process, known as boundary crossing, requires boundary objects; either artifacts, people, or institutions that play a bridging role between different boundary spaces. For this study, serious games powered by scientific models are identified as potentially effective boundary objects. A serious game simulation called Aqua Republica was used to organize game simulation events allowing stakeholders to connect in an in-person, informal and novel setting. This exploratory research aims to study the role and impact of serious games as boundary objects to enhancing collaboration and knowledge co-creation. The following research questions are addressed: (1) Do interactions increase over the course of a game simulation event? (2) Does the quality of interactions change over the course of a game simulation event? (3) Are the quantity and quality of interactions affected by pre-existing relationships? And if so, how? (4) How does the relationship between participants change over the course of a game simulation event? As part of this study, four game simulation events were organized that included students, professionals and diverse stakeholder groups working in watershed management contexts across Eastern Canada with 40 participants in total. Participants were divided into teams of 3-5 members and were surveyed and their interactions recorded. An interaction and social network analysis of the audiovisual recordings of each game simulation event indicates that interactions between participants increase in both quantity and quality as the game progresses. The analysis shows that serious game simulations provide an intervention platform not only to facilitate cross-boundary interactions, but also to strengthen relationships between diverse stakeholders, as expressed by an increase in mutual trust and empathy, as well as an improved understanding among the participants of the watershed system and the complex issues at stake., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Understanding precipitation on a regional basis is an important component of water resources planning and management. The present study outlines a methodology based on continuous wavelet transform (CWT) and multiscale entropy (CWME), combined with self-organizing map (SOM) and k-means clustering techniques, to measure and analyze the complexity of precipitation. Historical monthly precipitation data from 1960 to 2010 at 31 rain gauges across Iran were preprocessed by CWT. The multi-resolution CWT approach segregated the major features of the original precipitation series by unfolding the structure of the time series which was often ambiguous. The entropy concept was then applied to components obtained from CWT to measure dispersion, uncertainty, disorder, and diversification of subcomponents. Based on different validity indices, k-means clustering captured homogenous areas more accurately, and additional analysis was performed based on the outcome of this approach. The 31 rain gauges in this study were clustered into 6 groups, each one having a unique CWME pattern across different time scales. The results of clustering showed that hydrologic similarity (multiscale variation of precipitation) was not based on geographic contiguity. According to the pattern of entropy across the scales, each cluster was assigned an entropy signature that provided an estimation of the entropy pattern of precipitation data in each cluster. Based on the pattern of mean CWME for each cluster, a characteristic signature was assigned, which provided an estimation of the CWME of a cluster across scales of 1-2, 3-8, and 9-13 months relative to other stations. The validity of the homogeneous clusters demonstrated the usefulness of the proposed approach to regionalize precipitation. Further analysis based on wavelet coherence (WTC) was performed by selecting central rain gauges in each cluster and analyzing against temperature, wind, Multivariate ENSO index (MEI), and East Atlantic (EA) and North Atlantic Oscillation (NAO), indeces. The results revealed that all climatic features except NAO influenced precipitation in Iran during the 1960-2010 period., (Copyright © 2018 Elsevier Inc. All rights reserved.)

The data presented in this article is related to the research article entitled 'Serious games as a catalyst for boundary crossing, collaboration and knowledge co-creation in a watershed governance context' (Jean et al., In press) [1]. Understanding the team dynamics related to serious game simulations is critical for understanding the potential uses and functions of these simulations for knowledge co-creation (Medema et al., 2016) [2]. The data was obtained from four independent serious game simulation events and consists of n = 40 participants. Participants were divided into small teams and were then recorded playing the serious game Aqua Republica (http://aquarepublica.com/). Interactions were tallied and interaction maps created using the visualization software GEPHI (https://gephi.org/). The interaction maps allow for a visual representation of the progression of interactions over the course of four subsequent phases of gameplay (Jordan and Henderson, 1995) [3].

Chlorination, the basic treatment utilized for drinking water sources, is widely used for water disinfection and pathogen elimination in water distribution networks. Thereafter, the proper prediction of chlorine consumption is of great importance in water distribution network performance. In this respect, data mining techniques-which have the ability to discover the relationship between dependent variable(s) and independent variables-can be considered as alternative approaches in comparison to conventional methods (e.g., numerical methods). This study examines the applicability of three key methods, based on the data mining approach, for predicting chlorine levels in four water distribution networks. ANNs (artificial neural networks, including the multi-layer perceptron neural network, MLPNN, and radial basis function neural network, RBFNN), SVM (support vector machine), and CART (classification and regression tree) methods were used to estimate the concentration of residual chlorine in distribution networks for three villages in Kerman Province, Iran. Produced water (flow), chlorine consumption, and residual chlorine were collected daily for 3 years. An assessment of the studied models using several statistical criteria (NSC, RMSE, R 2 , and SEP) indicated that, in general, MLPNN has the greatest capability for predicting chlorine levels followed by CART, SVM, and RBF-ANN. Weaker performance of the data-driven methods in the water distribution networks, in some cases, could be attributed to improper chlorination management rather than the methods' capability.

Although Iran's Ghaen Plain provides saffron to much of the world, no regional groundwater quality (GQ) assessment has yet been undertaken. Given the region's potential for saltwater intrusion and heavy metal contamination, it is important to assess the GQ and determine its main probable source of pollution (MPSP). Such knowledge would allow for informed mitigation or elimination of the potential adverse health effects of this groundwater through its use as drinking water, or indirectly as a result of the consumption of groundwater-irrigated crops. Total dissolved solids, sodium, and chloride in the water of the majority of 16 wells sampled within the region exceeded World Health Organization and Iranian permissible standards for drinking water. The groundwater proved to only be suitable for irrigating salt tolerant crops under good drainage conditions. Due to the precipitation of calcium carbonate in the water supply facilities, the water from all wells was deemed unsuitable for industrial purposes. Heavy metal pollution and contamination indices showed no groundwater contamination. Analysis of ionic ratios and the application of principal components analysis indicated the MPSP to be saltwater intrusion, with the geology subtending the plain, and to a lesser extent, anthropogenic activities. Reducing groundwater withdrawals, particularly those for agricultural production by using high performance irrigation methods could reduce saltwater intrusion and improve GQ in the Ghaen Plain.

Waste sorting is key to the process of waste recycling. Exact identification of plastic resin and wood products using Near Infrared (NIR, 1-1.7µm) sensing is currently in use. Yet, dark targets characterized by low reflectance, such as black plastics, are hard to identify by this method. Following the recent success of Midwave Infrared (MWIR, 3-12µm) measurements to identify coloured plastic polymers, the aim of this study was to assess whether this technique is applicable to sorting black plastic polymers and wood products. We performed infrared reflectance contact measurements of 234 plastic samples and 29 samples of wood and paper products. Plastic samples included black, coloured and transparent Polyethylene Terephthalate (PET), Polyethylene (PE), Polyvinyl Chloride (PVC), Polypropylene (PP), Polylactic acid (PLA) and Polystyrene (PS). The spectral signatures of the black and coloured plastic samples were compared with clear plastic samples and signatures documented in the literature to identify the polymer spectral features in the presence of coloured material. This information was used to determine the spectral bands that best suit the sorting of black plastic polymers. The main NIR-MWIR absorption features of wood, cardboard and paper were identified as well according to the spectral measurements. Good agreement was found between our measurements and the absorption features documented in the literature. The new approach using MWIR spectral features appears to be useful for black plastics as it overcomes some of the limitations in the NIR region to identify them. The main limitation of this technique for industrial applications is the trade-off between the signal-to-noise ratio of the sensor operating in standoff mode and the speed at which waste is moved under the sensor. This limitation can be resolved by reducing the system's spectral resolution to 16cm -1 , which allows for faster spectra acquisition while maintaining a reasonable signal-to-noise ratio., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

An investigation of the scaling characteristics of vegetation and temperature data derived from LANDSAT data was undertaken for a heterogeneous area in Tamil Nadu, India. A wavelet-based multiresolution technique decomposed the data into large-scale mean vegetation and temperature fields and fluctuations in horizontal, diagonal, and vertical directions at hierarchical spatial resolutions. In this approach, the wavelet coefficients were used to investigate whether the normalized difference vegetation index (NDVI) and land surface temperature (LST) fields exhibited self-similar scaling behaviour. In this study, l-moments were used instead of conventional simple moments to understand scaling behaviour. Using the first six moments of the wavelet coefficients through five levels of dyadic decomposition, the NDVI data were shown to be statistically self-similar, with a slope of approximately -0.45 in each of the horizontal, vertical, and diagonal directions of the image, over scales ranging from 30 to 960 m. The temperature data were also shown to exhibit self-similarity with slopes ranging from -0.25 in the diagonal direction to -0.20 in the vertical direction over the same scales. These findings can help develop appropriate up- and down-scaling schemes of remotely sensed NDVI and LST data for various hydrologic and environmental modelling applications. A sensitivity analysis was also undertaken to understand the effect of mother wavelets on the scaling characteristics of LST and NDVI images.

The spin filtering effect in the bilayer nanowire with quantum point contact is investigated theoretically. We demonstrate the new mechanism of the spin filtering based on the lateral inter-subband spin-orbit coupling, which for the bilayer nanowires has been reported to be strong. The proposed spin filtering effect is explained as the joint effect of the Landau-Zener intersubband transitions caused by the hybridization of states with opposite spin (due to the lateral Rashba SO interaction) and the confinement of carriers in the quantum point contact region.

Due to inherent uncertainties in measurement and analysis, groundwater quality assessment is a difficult task. Artificial intelligence techniques, specifically fuzzy inference systems, have proven useful in evaluating groundwater quality in uncertain and complex hydrogeological systems. In the present study, a Mamdani fuzzy-logic-based decision-making approach was developed to assess groundwater quality based on relevant indices. In an effort to develop a set of new hybrid fuzzy indices for groundwater quality assessment, a Mamdani fuzzy inference model was developed with widely-accepted groundwater quality indices: the Groundwater Quality Index (GQI), the Water Quality Index (WQI), and the Ground Water Quality Index (GWQI). In an effort to present generalized hybrid fuzzy indices a significant effort was made to employ well-known groundwater quality index acceptability ranges as fuzzy model output ranges rather than employing expert knowledge in the fuzzification of output parameters. The proposed approach was evaluated for its ability to assess the drinking water quality of 49 samples collected seasonally from groundwater resources in Iran's Sarab Plain during 2013-2014. Input membership functions were defined as "desirable", "acceptable" and "unacceptable" based on expert knowledge and the standard and permissible limits prescribed by the World Health Organization. Output data were categorized into multiple categories based on the GQI (5 categories), WQI (5 categories), and GWQI (3 categories). Given the potential of fuzzy models to minimize uncertainties, hybrid fuzzy-based indices produce significantly more accurate assessments of groundwater quality than traditional indices. The developed models' accuracy was assessed and a comparison of the performance indices demonstrated the Fuzzy Groundwater Quality Index model to be more accurate than both the Fuzzy Water Quality Index and Fuzzy Ground Water Quality Index models. This suggests that the new hybrid fuzzy indices developed in this research are reliable and flexible when used in groundwater quality assessment for drinking purposes., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

In a previous work, the ultrasonic measurement of longitudinal strain in a plate using the Time Reversal technique was proved. One drawback of this measurement is the low sensitivity of the signal against changes in strain. This problem can be solved using the inverse filter signal processing. This technique increases sensitivity but also reduces the energy of the signal and, consequently, the signal to noise ratio. Thus, a physical solution is presented in order to improve the sensitivity of the system. Additionally, the one-bit time reversal is introduced in order to simplify the hardware used in this technique. The strain sensing system is composed of a pair of piezocomposite transducers bonded to the surface of the tested plate and used to generate and sense the ultrasonic waves guided through the specimen. The use of time reversal provides phase compensation for dispersion and edge reflections in the propagation of the guided waves in the plate, allowing time recompression of the waves. The measurement principle is based on the detection of changes in the amplitude and time-of-flight of the focused signal when the plate is subjected to longitudinal strains. System sensitivity is improved by using 2-2 piezocomposite transducers designed to operate between 0.2 to 3.0 MHz. In the signal processing, the one-bit time reversal is compared with the conventional time reversal in twelve-bit resolution. A figure of merit is introduced in order to evaluate the influence of the transfer function on strain sensitivity. This figure of merit relates the energy concentrated at the time reversal focus with the total energy of the signal. This value represents the ability of the time reversal process to recompress the signal at the focus. Experiments were conducted by applying strains up to 150 μm/m. Results show a linear response in the change of the focus amplitude. The sensitivity depends on the transducers and it can be related to the proposed figure of merit. The focus quality is kept when one-bit time reversal is used, showing to be also feasible for the measuring technique. All the results agreed with the numerical time-reversal implementation.

Identification of potential sites for rainwater harvesting (RWH) is an important step toward maximizing water availability and land productivity in arid semiarid regions. Characterised as a "water scarce" country, Egypt has limited fresh water supplies, and is expected to suffer from water stress by the year 2030. Therefore, it is important to develop any means available to supply water and maintain human habitability in a sustainable manner. Practiced or simply indispensable in many countries around the world, rainwater harvesting (RWH) promotes a sustainable and efficient manner of exploiting water resources. In the present study, suitable areas for sustainable stormwater harvesting and storage in Egypt were identified using remote sensing for land cover data - location assessment linked to a decision support system (DSS). The DSS took into consideration a combination of thematic layers such as rainfall surplus, slope, potential runoff coefficient (PRC), land cover/use, and soil texture. Taking into account five thematic layers, the spatial extents of RWH suitability areas were identified by an analytical hierarchy process (AHP). The model generated a RWH map with five categories of suitability: excellent, good, moderate, poor and unsuitable. The spatial distribution of these categories in the area investigated was such that 4.8% (47910 km(2)) and 14% (139739 km(2)) of the study area was classified as excellent or good in terms of RWH, respectively, while 30.1% (300439 km(2)), 47.6% (474116 km(2)) and 3.5% (34935 km(2)) of the area were classified as moderate, unsuitable and poor, respectively. Most of the areas with excellent to good suitability had slopes of between 2% and 8% and were intensively cultivated areas. The major soil type in the excellent suitability areas was loam, while rainfall ranged from 100 to 200 mm yr(-1). The use of a number of RWH sites in the excellent areas is recommended to ensure successful implementation of RWH systems.