Crop water footprints and their driving mechanisms show regional differences.

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 Gm3, 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.

Development of a behaviour pattern-based testing approach for coupled socioeconomic and environmental models.

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.

Elevation-dependent intensification of fire danger in the western United States.

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.

Multi-objective optimization of hydrant flushing in a water distribution system using a fast hybrid technique.

As a critical element in preserving the health of urban populations, water distribution systems (WDSs) must be ready to implement emergency plans when catastrophic events such as contamination events occur. A risk-based simulation-optimization framework (EPANET-NSGA-III) combined with a decision support model (GMCR) is proposed in this study to determine optimal locations for contaminant flushing hydrants under an array of potentially hazardous scenarios. Risk-based analysis using Conditional Value-at-Risk (CVaR)-based objectives can address uncertainties regarding the mode of WDS contamination, thereby providing a robust plan to minimize the associated risks at a 95% confidence level. Conflict modeling by GMCR achieved an optimal compromise solution within the Pareto front by identifying a final stable consensus among the decision-makers involved. A novel hybrid contamination event grouping-parallel water quality simulation technique was incorporated into the integrated model to reduce model runtime, the main deterrent in optimization-based methods. The nearly 80% reduction in model runtime made the proposed model a viable solution for online simulation-optimization problems. The framework's capacity to address real-world problems was evaluated for the WDS operating in Lamerd, a city in Fars Province, Iran. Results showed that the proposed framework was capable of highlighting a single flushing strategy, which not only optimally reduced risks associated with contamination events, but provided acceptable coverage against such threats, flushing 35-61.3% of input contamination mass on average, and reducing average time-to-return to normal conditions by 14.4-60.2%, while employing less than half of the initial potential hydrants.

Leaf Stoichiometry of Potentilla fruticosa Across Elevations in China's Qilian Mountains.

As an individual plant species can develop its own leaf stoichiometry to adapt to environmental changes, this stoichiometry can provide critical information about a plant species' growth and its potential management in the ecosystem housing it. However, leaf stoichiometry is largely undocumented in regions with large environmental changes arising from differences in elevation. The leaf stoichiometry of Potentilla fruticosa L., a major alpine shrub playing an important role in supporting ecosystem functions and services in China's Qilian Mountains (Northeast Qinghai-Tibetan Plateau), was investigated at different elevations (2,400, 2,600, 2,800, 3,000, 3,200, 3,500, and 3,800 m). At each elevation, leaf elemental (C, N, and P) concentrations were measured in P. fruticosa leaves sampled from three plots (10 × 10 m), and edaphic properties were assessed in nine quadrats (1 × 1 m, three quadrats per plot). Temperature and precipitation were calculated using an empirical formula. Maximum and minimum leaf carbon (C) concentrations ([C] leaf ) of 524 ± 5.88 and 403 ± 3.01 g kg-1 were measured at 2,600 and 3,500 m, respectively. Leaf nitrogen (N) concentration ([N] leaf ) showed a generally increasing trend with elevation and peaked at 3,500 m (27.33 ± 0.26 g kg-1). Leaf phosphorus (P) concentration ([P] leaf ) varied slightly from 2,400 to 3,200 m and then dropped to a minimum (0.60 ± 0.10 g kg-1) at 3800 m. The [C] leaf :[N] leaf , [C] leaf :[P] leaf , and [N] leaf :[P] leaf varied little from 2,400 to 3,000 m but fluctuated somewhat at higher elevations. The main factors affecting P. fruticosa leaf stoichiometry were soil organic C, pH, and soil total P, and the main limiting element for the growth of P. fruticosa in the study area was P. In conclusion, changes in elevation affected leaf stoichiometry of P. fruticosa mainly due to altered soil properties, and addressing phosphorus limitation, especially at higher elevations mainly due to losses caused by high precipitation and sparse vegetation, is a key measure to promote P. fruticosa growth in this region.

Integrated assessment of localized SSP-RCP narratives for climate change adaptation in coupled human-water systems.

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.

Warming enabled upslope advance in western US forest fires.

Increases in burned area and large fire occurrence are widely documented over the western United States over the past half century. Here, we focus on the elevational distribution of forest fires in mountainous ecoregions of the western United States and show the largest increase rates in burned area above 2,500 m during 1984 to 2017. Furthermore, we show that high-elevation fires advanced upslope with a median cumulative change of 252 m (-107 to 656 m; 95% CI) in 34 y across studied ecoregions. We also document a strong interannual relationship between high-elevation fires and warm season vapor pressure deficit (VPD). The upslope advance of fires is consistent with observed warming reflected by a median upslope drift of VPD isolines of 295 m (59 to 704 m; 95% CI) during 1984 to 2017. These findings allow us to estimate that recent climate trends reduced the high-elevation flammability barrier and enabled fires in an additional 11% of western forests. Limited influences of fire management practices and longer fire-return intervals in these montane mesic systems suggest these changes are largely a byproduct of climate warming. Further weakening in the high-elevation flammability barrier with continued warming has the potential to transform montane fire regimes with numerous implications for ecosystems and watersheds.

The role of climate change and vegetation greening on the variation of terrestrial evapotranspiration in northwest China's Qilian Mountains.

Terrestrial evapotranspiration (ETa) reflects the complex interactions of climate, vegetation, soil and terrain and is a critical component in water and energy cycles. However, the manner in which climate change and vegetation greening influence ETa remains poorly understood, especially in alpine regions. Drawing on the Global Land Evaporation Amsterdam Model (GLEAM) ETa data, the interannual variability of ETa and its ties to precipitation (P), potential evaporation (ETp) and vegetation (NDVI) were analysed. The Budyko framework was implemented over the period of 1982 to 2015 to quantify the response of ETa to climate change's direct (P and ETp) and indirect (NDVI) impacts. The ETa, P, ETp and NDVI all showed significant increasing trends from 1981 to 2015 with rates of 1.52 mm yr-1, 3.18 mm yr-1, 0.89 mm yr-1 and 4.0 × 10-4 yr-1, respectively. At the regional level, the positive contribution of increases in P and NDVI offset the negative contribution of ETp to the change in ETa (∆ETa). The positive ∆ETa between 1982 and 2001 was strongly linked with the concomitant increase in NDVI. Increases in vegetation contributing to a positive ∆ETa differed among landscape types: for shrub, meadow and steppe they occurred during both periods, for alpine vegetation between 1982 and 2001, and for desert between 2002 and 2015. Climate change directly contributed to a rise in ETa, with P as the dominant factor affecting forested lands during both periods, and alpine vegetation between 2002 and 2015. Moreover, ETp was a dominant factor for the desert between 1982 and 2001, where the variation of P was not significant. The contributions of factors having an impact on ∆ETa are modulated by both the sensitivity of impact factors acting on ETa as well as the magnitudes of factor changes. The greening of vegetation can influence ETa by increasing vegetation transpiration and rainfall interception in forest, brush and meadow landscapes. These findings can help in developing a better understanding of the interaction of ecosystems and hydrology in alpine regions.

Participatory Modeling of Water Vulnerability in Remote Alaskan Households Using Causal Loop Diagrams.

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.

A century of observations reveals increasing likelihood of continental-scale compound dry-hot extremes.

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.

Causality of climate, food production and conflict over the last two millennia in the Hexi Corridor, China.

The relationship between climate and human society has frequently been investigated to ascertain whether climate variability can trigger social crises (e.g., migration and armed conflicts). In the current study, statistical methods (e.g., correlation analysis and Granger Causality Analysis) are used in a systematic analysis of the potential causality of climate variability on migration and armed conflicts. Specifically, the statistical methods are applied to determine the relationships between long-term fine-grained temperature and precipitation data and contemporary social conditions, gleaned from historical documents covering the last two millennia in China's Hexi Corridor. Results found the region's reconstructed temperature to be strongly coupled with precipitation dynamics, i.e., a warming climate was associated with a greater supply of moisture, whereas a cooling period was associated with more frequent drought. A prolonged cold period tended to coincide with societal instability, such as a shift from unification towards fragmentation. In contrast, a prolonged warm period coincided with rapid development, i.e., a shift from separation to unification. The statistical significance of the causality linkages between climate variability, bio-productivity, grain yield, migration and conflict suggests that climate variability is not the direct causative agent of these phenomena, but that climate reduced food production which gradually lead to migration and conflicts. A conceptual causal model developed through this study describes the causative pathway of climate variability impacts on migration and conflicts in the Hexi Corridor. Applied to current conditions, the model suggests that steady and proactive promotion of the nation's economic buffering capacity might best address the uncertainty brought on by a range of potential future climate scenarios and their potential impacts.

Modification of the DRASTIC Framework for Mapping Groundwater Vulnerability Zones.

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 (NO3 ) 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 NO3 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 NO3 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 R2 of 0.22, the Wilcoxon-GA-DRASTIC obtained a maximum R2 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 R2 value to the highest degree.

Stochastic Modeling of Groundwater Fluoride Contamination: Introducing Lazy Learners.

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 Ca2+ 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.

Analyzing trends of days with low atmospheric visibility in Iran during 1968-2013.

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 (DAV2) 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 DAV2 were detected in the central, east, and northeast regions of the country. Analyzing the DAV2 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.

Effects of stand age on carbon storage in dragon spruce forest ecosystems in the upper reaches of the Bailongjiang River basin, China.

At an ecosystem level, stand age has a significant influence on carbon storage (CS). Dragon spruce (Picea asperata Mast.) situated along the upper reaches of the Bailongjiang River in northwest China were categorized into three age classes (29-32 years, Y1; 34-39 years, Y2; 40-46 years, Y3), and age-related differences in total carbon storage (TCS) of the forest ecosystem were investigated for the first time. Results showed that TCS for the Y1, Y2, and the Y3 age groups were 323.64, 240.66 and 174.60 Mg ha-1, respectively. The average TCS of the three age groups was 255.65 Mg C ha-1, with above-ground biomass, below-ground biomass, litter, and soil in the top 0.6 m contributing 15.0%, 3.7%, 12.1%, and 69.2%, respectively. CS in soil and TCS of the Y1 age group both significantly exceeded those of the Y3 age group (P < 0.05). Contrary to other recent findings, the present study supports the hypothesis that TCS is likely to decrease as stand age increases. This indicates that natural resource managers should rejuvenate forests by routinely thinning older stands, thereby not only achieving vegetation restoration, but also allowing these stands to create a long-term carbon sink for this important eco-region.

Delimitation of groundwater zones under contamination risk using a bagged ensemble of optimized DRASTIC frameworks.

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 (NO3) 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 NO3 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.

Modeling sustainability visions: A case study of multi-scale food systems in Southwestern Ontario.

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.

Modified-DRASTIC, modified-SINTACS and SI methods for groundwater vulnerability assessment in the southern Tehran aquifer.

This study aims to modify the SINTACS and DRASTIC models with a land-use (LU) layer and compares the modified-DRASTIC, modified-SINTACS and SI methods for groundwater vulnerability assessment (GVA) in the southern Tehran aquifer, Iran. Single parameter sensitivity analysis (SPSA) served to determine the most significant parameters for the modified-DRASTIC, modified-SINTACS and SI approaches, and to revise model weights from "theoretical" to "effective." The inherent implementation of LU in the SI model may explain its better performance compared to unenhanced versions of DRASTIC and SINTACS models. Validation of all models, using nitrate concentrations from 20 wells within the study area, showed the modified-SINTACS model to outperform other models. The SPSA showed that the vadose zone and LU strongly influenced the modified-DRASTIC and modified-SINTACS models, while SI was strongly influenced by aquifer media and LU. To improve performance, models were implemented using "effective" instead of "theoretical" weights. Model robustness was assessed using nitrate concentrations in the aquifer and the outcomes confirmed the positive impact of using "effective" versus "theoretical" weights in the models. Modified-SINTACS showed the strongest correlation between nitrate and the vulnerability index (coefficient of determination = 0.75). Application of the modified-SINTACS while using "effective" weights, led to the conclusion that 19.6%, 55.2%, 23.4%, and 1.6% of the study area housed very high, high, moderate and low vulnerability zones, respectively.

Assessing the potential origins and human health risks of trace elements in groundwater: A case study in the Khoy plain, Iran.

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.

An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines.

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.