Interactive effects of water quality, physical habitat, and watershed anthropogenic activities on stream ecosystem health

An extended novel approach of river health analysis using nonmetric scaling ordination of fish ecological entities and functional identity indices

As river ecosystems continue to face environmental pressures, it is crucial to develop and apply new biologically relevant statistical tools to support river health assessments. This study aimed to test the potential significance of nonmetric ordination scores and multidimensional functional indices of fish communities for analyzing river ecological health linked to variations in environmental factors. We conducted a nonmetric multidimensional scaling (NMDS) to derive ordination scores based on twenty fish ecological entities (FEs) that we newly defined, along with their relative abundance (RA). These FEs were determined through a unique combination of trophic, habitat, and tolerance guilds. Additionally, we calculated the functional diversity and identity (F-Ide) indices using the RA-weighted distances between FEs defined by the guild identities in a multidimensional space. The results showed that these novel analyses were helpful in assessing river ecological health and spatial changes in the environment. The dominant F-Ide in each community was largely responsible for the variation of river health scores, driven by the combined impact of chemical water quality, alterations in substrate composition, land use changes, and the gradient of elevation. These environmental factors significantly influenced the site scores in the first axis of NMDS, F-Ide indices, and river health scores. Therefore, our study highlights the practical value of utilizing nonmetric scaling ordination scores of FEs and F-Ide indices as tools for evaluating the health of river ecosystems. By elucidating fish community variance, these tools can aid in determining the extent of river health degradation attributable to environmental stressors, including chemical water pollution and changes in substrate resulting from changes in land use and at different elevations.

Watershed health and ecological security zoning throughout Iran

Today, land degradation and the decrease in the expected services of watersheds have been mainly influenced by human-induced activities. Hence, it requires more attention to adaptively manage and provide feasible solutions to watershed disruptions. However, appropriate management of precious commodities such as water, soil, air, and vegetation cover needs insight planning on a proper scale. Nonetheless, such an integrated approach to comprehensive health assessment of watershed resources is yet to be indoctrinated by scholars, implemental agencies, managers, and policymakers. Accordingly, the present endeavor has tried to evaluate the health status of Iran's 30 second-order large watersheds with the pressure-state-response (PSR) approach. In this regard, 44 problem-oriented, influential, and, at the same time, accessible variables with compatible scales at the national level were primarily determined in climatic, hydrologic, anthropogenic, and natural sectors. The collinearity-free and independent variables were then finalized using the variance inflation factor (VIF) test. Ultimately, P, S, and R indices were calculated using the arithmetic mean of 25 normalized variables based on which PSR-based health and security indices were also mapped countrywide. The results indicated that P, S, and R indices varied from 0.49 to 0.69, 0.42 to 0.82, and 0.40 to 0.94, respectively. Health and security indices ranged from 0.46 to 0.69 and 0.30 to 0.89, respectively. The weighted mean of P, S, and R was 0.59, 0.62, and 0.67, respectively, wholly placing them in the intermediate class. The weighted health and security indices were also 0.58 and 0.59, representing the intermediate class. The results showed that study watersheds had different health and security conditions from interplaying watershed-specific factors. The results revealed the necessity of watershed-unique managerial strategies to cope with the existing unfavorable conditions at the country level. However, further insight with high resolution is recommended for the high-priority watersheds to plan implementation and executive projects.

Assessing hydrological connectivity for natural-artificial catchment with a new framework integrating graph theory and network analysis

Anthropogenic activities alter the underlying surface conditions and arrangements of landscape features in a drainage basin, interfering with the pollutant (e.g., dissolved nitrogen, phosphorus) transport network configuration and altering the hydrological response. Assessing the impact of anthropogenic activities on hydrological connectivity for natural-artificial catchment is critical to understand the hydrological-driven ecosystem processes, services and biodiversity. However, quantifying this impact at catchment scale remains challenging. In this study, a new framework was proposed to quantify the impact of anthropogenic activities on hydrological connectivity combined with graph theory and network analysis. This framework was exemplified in a natural-artificial catchment of the Yangtze River basin of China. Based on remote sensing and field-investigated data, three transport networks were constructed, including natural transport network (N1), ditch-road transport network (N2), and terrace-dominated transport network (N3), which reflected the different human intervention. The results showed that human intervention improved the connectivity of the nodes and enhanced the complexity of the catchment transport network structure. Anthropogenic activities significantly decreased the hydrological structural connectivity of the catchment. In particular, compared with the N1 network, the critical nodes for hydrological connectivity which were judged by connectivity indexes were reduced by 92.94% and 95.29% in the N2 and N3 network, respectively. Furthermore, the ditch-road construction had a greater impact than terraces in decreasing hydrological structural connectivity at catchment scale. This framework has proven effective in quantifying the hydrological connectivity analysis under different human intervention at the catchment scale and facilitates the improvement of catchment management strategies.

Identifying Key Stressors Driving Biological Impairment in Freshwater Streams in the Chesapeake Bay Watershed, USA

Biological communities in freshwater streams are often impaired by multiple stressors (e.g., flow or water quality) originating from anthropogenic activities such as urbanization, agriculture, or energy extraction. Restoration efforts in the Chesapeake Bay watershed, USA seek to improve biological conditions in 10% of freshwater tributaries and to protect the biological integrity of existing healthy watersheds. To achieve these goals, resource managers need to better understand which stressors are most likely driving biological impairment. Our study addressed this knowledge gap through two approaches: 1) reviewing and synthesizing published multi-stressor studies, and 2) examining 303(d) listed impairments linked to biological impairment as identified by jurisdiction regulatory agencies (the states within the watershed and the District of Columbia). Results identified geomorphology (i.e., physical habitat), salinity, and toxic contaminants as important for explaining variability in benthic community metrics in the literature review. Geomorphology (i.e., physical habitat and sediment), salinity, and nutrients were the most reported stressors in the jurisdictional impairment analysis. Salinity is likely a major stressor in urban and mining settings, whereas geomorphology was commonly reported in agricultural settings. Toxic contaminants, such as pesticides, were rarely measured; more research is needed to quantify the extent of their effects in the region. Flow alteration was also highlighted as an important urban stressor in the literature review but was rarely measured in the literature or reported by jurisdictions as a cause of impairment. These results can be used to prioritize stressor monitoring by managers, and to improve stressor identification methods for identifying causes of biological impairment.

Watershed health assessment using the coupled integrated multistatistic analyses and PSIR framework

Quantitatively assessing watershed health under anthropogenic activities and management responses is important for the scientific planning and management of watersheds. The current research on watershed health assessments insufficiently reflects watershed scale information from different dimensions, which leads to the incomplete understanding of watersheds and thus the lack of systematic management. This study investigated the health status in the Chaohu Lake watershed (CLW) based on monthly sampling data at 46 river sites in 2018. Watershed health assessment comprehensively considered four dimensions including socioeconomic and natural pressures, nonpoint pollution export, river water quality and management responses with the pressure-state-impact-response (PSIR) framework. Canonical correlation analysis (CCA) and variance partitioning analysis (VPA) were integrated to further quantify the inter-relationships among the variables of each PSIR index. An obstacle degree model was applied to examine the factors of mainly affecting the status of watershed health. The results showed that phosphorus, nitrogen and sediment exports of CLW increased more and river water quality in CLW worsened due to socioeconomic and natural pressures. Water quality improvement effectively responds to increasing woodland and grassland. Compared with natural factors, phosphorus, nitrogen and sediment exports had closer relationships with the pressures from socioeconomic activities. Moreover, socioeconomic pressures explained more changes in phosphorus and nitrogen exports, while natural factors explained relatively more changes in sediment exports. Phosphorus, nitrogen and sediment exports and woodland and grassland coverage explained <35 % of the variation in river water quality. Additionally, the obstacle degrees of pressures and phosphorus, nitrogen and sediment exports were lower, and the obstacle degrees of river water quality and woodland and grassland coverage were higher in urban sub-watersheds, which was the opposite in agricultural sub-watersheds. This research provides a new evaluation framework of watershed health and its obstacle factors, which is crucial to improve watershed health.

River habitat assessment and restoration in high dam flood discharge systems with total dissolved gas supersaturation

The success of river habitat restoration relies on accurate assessment proxies. However, determining how to quantitatively assess the impact of multiple stressors during flood discharge from high dams in riverine ecosystems and where and how to implement more reliable recovery interventions remain challenges. Here, we developed a bottom-up mechanistic framework for assessing the effects of total dissolved gas supersaturation (TDGS) and hydrodynamics on fish habitat quality and applied it to the downstream river reach of the Xiangjiaba Dam in Southwest China. The results showed that the available habitat area of river sturgeon was the smallest, while Chinese sucker had the largest available habitat area among the three target species under all discharge scenarios. Although the TDGS levels were evenly mixed laterally, the habitat suitability index indicated that the suitable habitats were primarily within both sides of the river reach under all scenarios, which is contrary to findings based on the traditional TDGS risk assessment model. The traditional TDGS risk assessment model overestimates the impact of dams on habitats. This divergence reflected the sensitivity of the habitat assessment to fish habitat preferences, fish tolerance to TDGS and the biological response of fish under TDGS. Additionally, the priority areas for restoration can be identified by habitat suitability index with lower values. We simulated twenty-four schemes and found that interventions such as stone groups, ecological spur dike, water-retaining weir and river dredging can enhance habitat suitability for fish species under multiple stressors, providing novel insights into where and how to mitigate the impact of TDGS. Our findings offer a transferable framework for the quantitative evaluation of fish habitat and implementation of restoration management during dam flood discharge periods, thus providing a new perspective for biodiversity conservation and habitat restoration in dam-regulated rivers with TDGS around the world.

Mining footprint of the underground longwall caving extraction method: A case study of a typical industrial coal area in China

Longwall caving mining (LCM) can lead to many environmental problems that have drawn worldwide attention. A previous survey found that most scholars tend to analyze the two issues separately, that is, coal mining-induced subsidence and heavy metal pollution sources in the soil of the mining regions. Based on field monitoring as well as the collection and analysis of soil samples, a previous study estimated ground settlement and analyzed the surface subsidence law and spatial distribution characteristics of heavy metals in soils. Moreover, a geographic information system was combined with multivariate statistical analysis methods to analyze the heavy metal pollution sources in soils. At the same time, the mechanism of heavy metal accumulation in the subsidence area was analyzed. The study found that the most active subsidence of settlement was 137.5 m behind the workface and moved forward with the workface. LCM has already caused significant disturbance to the soils in the Hengyuan Mine. Moreover, the distribution pattern of eight heavy metals was consistent with the surface subsidence law. The sources of heavy metal pollution in the soils were also identified; namely, coal mining-induced subsidence (64.1%) and mixed transportation and wind-mediated spread (35.9%), offering a reinterpretation of the LCM's footprint.

Incorporating the life stages of fish into habitat assessment frameworks: A case study in the Baihetan Reservoir

Although it is widely accepted that the construction of dams may alter fish habitats, few studies have followed the life cycles of fish and combined the environmental conditions with the ecological behaviors and habit preferences of fish during reproductive processes to assess its effects of dam construction. In this study, we call for more sophisticated and holistic assessment framework, including effectiveness of technologies intended to mitigate environmental impacts in different life stages. An assessment framework that considers the swimming ability, perception ability of water flow and environmental preference of different fish species during migration, spawning and hatching was proposed. We used the Baihetan Reservoir as an example environment to assess the impoundment effect on the habitat of a tributary upstream of the reservoir. We observed shifts in the habitats of target fish in different life stages which is dominated by reservoir operation of the Baihetan Dam. Combined with the response of fish activities to impoundment, the selection of suitable positions for artificial breeding and release projects and the outlet of the fish transportation system were recommended measures to improve the migration possibilities. Our reassessment results also demonstrated the theoretical possibility and feasibility of joint improvements in spawning and hatching periods using instream structures. Our framework provides a complete set of "assessment-solution" processes for developers and managers to address the aquatic ecological degradation caused by resource development, and its use is strongly recommended for assessments or assessments of damming effects in other regions and on other fish species.

Prediction of Aquatic Ecosystem Health Indices through Machine Learning Models Using the WGAN-Based Data Augmentation Method

Changes in hydrological characteristics and increases in various pollutant loadings due to rapid climate change and urbanization have a significant impact on the deterioration of aquatic ecosystem health (AEH). Therefore, it is important to effectively evaluate the AEH in advance and establish appropriate strategic plans. Recently, machine learning (ML) models have been widely used to solve hydrological and environmental problems in various fields. However, in general, collecting sufficient data for ML training is time-consuming and labor-intensive. Especially in classification problems, data imbalance can lead to erroneous prediction results of ML models. In this study, we proposed a method to solve the data imbalance problem through data augmentation based on Wasserstein Generative Adversarial Network (WGAN) and to efficiently predict the grades (from A to E grades) of AEH indices (i.e., Benthic Macroinvertebrate Index (BMI), Trophic Diatom Index (TDI), Fish Assessment Index (FAI)) through the ML models. Raw datasets for the AEH indices composed of various physicochemical factors (i.e., WT, DO, BOD5, SS, TN, TP, and Flow) and AEH grades were built and augmented through the WGAN. The performance of each ML model was evaluated through a 10-fold cross-validation (CV), and the performances of the ML models trained on the raw and WGAN-based training sets were compared and analyzed through AEH grade prediction on the test sets. The results showed that the ML models trained on the WGAN-based training set had an average F1-score for grades of each AEH index of 0.9 or greater for the test set, which was superior to the models trained on the raw training set (fewer data compared to other datasets) only. Through the above results, it was confirmed that by using the dataset augmented through WGAN, the ML model can yield better AEH grade predictive performance compared to the model trained on limited datasets; this approach reduces the effort needed for actual data collection from rivers which requires enormous time and cost. In the future, the results of this study can be used as basic data to construct big data of aquatic ecosystems, needed to efficiently evaluate and predict AEH in rivers based on the ML models.

A data-driven framework for spatiotemporal characteristics, complexity dynamics, and environmental risk evaluation of river water quality

To evaluate the evolution of river water quality in a changing environment, measuring the objective water quality is critical for understanding the rules of river water pollution. Based on the sample entropy theory and a nonlinear statistical method, this study aims to identify the spatiotemporal dynamics of water quality and its complexity in the Yangtze River basin using time series data, to separate the contributions of human activity and climate change to water quality, and to establish a data-driven risk assessment framework for the spatial (potential risk) and temporal (direct risk) aspects of water pollution. The results demonstrate that the spatiotemporal dynamics of water quality and sample entropy in each monitoring section are closely related to the characteristics of the corresponding location. The water quality of the main stream is superior, and its complexity is less than that of the tributaries. Cascade reservoir operation and vegetation status, agricultural production, and rainfall patterns exert great influences in the upper, middle, and lower reaches, respectively. Dam construction, urban agglomeration development, and interactions between river and lake are also influencing factors. An attributional analysis found that climate change and human activities negatively contributed to the evolution of NH₃-N concentration in most of the monitored sections, and the average relative contribution rates of human activities to changes in water quality in the main and tributary streams were -55.46% and -48.49%, respectively. In addition, the construction of data-driven risk assessment framework can efficiently and accurately assess the potential and direct water pollution risks of rivers.

Linking bait and feeding opportunities to fish foraging habitat for the assessment of environmental flows and river restoration

The survival of aquatic biota in different life history stages depends on food availability, water quantity and specific hydrological conditions, and is particularly susceptible in degraded rivers due to the development of hydropower or are sensitive to climate change. Habitats with limited food availability and restricted feeding opportunities can strongly affect the habitat carrying capacity and fish growth with consequences for spawning. Few environmental flow regime frameworks are available that closely link bait and feeding opportunities to fish foraging habitat. In addition, river restoration has been widely implemented to resolve the conflict between ecological demand and power generation benefits. Nevertheless, whether in-stream structures are still suitable for the joint operation of foraging and spawning habitats remains unclear. In this study, a framework to integrate the requirements of both spawning and foraging habitats into environmental flow regime assessments was proposed by coupling the bait supply, fish spawning and fish feeding opportunities. Here, we used the Batang Reservoir, located in the Tibetan Plateau, as an example to determine the environmental flow regimes. The environmental flow regimes during Periods I, II and III for the conservation of the life history stages of Schizothorax dolichonem were determined, which provided high-quality food and was beneficial for increasing the probability of restoration success. After the implementation of measures, the ecological base flow rate decreased from 171.80 m³/s, 206.00 m³/s and 257.70 m³/s to 138.00 m³/s, 206.00 m³/s and 206.00 m³/s in Periods I, II and III, respectively. We concluded that traditional river restoration with the use of in-stream structures is still suitable for the joint operation of spawning and foraging habitats, but the design selection and placement of in-stream structures should be preoptimized. The framework proposed will help managers evaluate habitat conservation to protect degraded rivers or help develop strategies to build resilience to climate change.

Evaluating the Relationships between Riparian Land Cover Characteristics and Biological Integrity of Streams Using Random Forest Algorithms

The relationships between land cover characteristics in riparian areas and the biological integrity of rivers and streams are critical in riparian area management decision-making. This study aims to evaluate such relationships using the Trophic Diatom Index (TDI), Benthic Macroinvertebrate Index (BMI), Fish Assessment Index (FAI), and random forest regression, which can capture nonlinear and complex relationships with limited training datasets. Our results indicate that the proportions of land cover types in riparian areas, including urban, agricultural, and forested areas, have greater impacts on the biological communities in streams than those offered by land cover spatial patterns. The proportion of forests in riparian areas has the greatest influence on the biological integrity of streams. Partial dependence plots indicate that the biological integrity of streams gradually improves until the proportion of riparian forest areas reach about 60%; it rapidly decreases until riparian urban areas reach 25%, and declines significantly when the riparian agricultural area ranges from 20% to 40%. Overall, this study highlights the importance of riparian forests in the planning, restoration, and management of streams, and suggests that partial dependence plots may serve to provide insightful quantitative criteria for defining specific objectives that managers and decision-makers can use to improve stream conditions.

Developing a statistical-weighted index of biotic integrity for large-river ecological evaluations

The efficiency, accuracy and universality of ecological assessment methods comprise an important foundation for comprehensive assessment and restoration of large river ecological health at the watershed scale. New evaluation metrics and methods are urgently needed to be developed to adapt the characteristics of large rivers, including geographical differences in surface runoff, regional ecological complexity, and seasonal changes. In this study, a bacteria-weighted index of biotic integrity was developed to assess the ecological health of large rivers (lrBW-IBI) based on compositional and functional characteristics of sediment bacterial communities from 33 sections of the lower mainstream of Yangtze River. Five key metrics were determined by range, responsiveness, and redundancy tests. Principal component analysis (PCA), entropy method, criteria importance through intercriteria correlation and random forest were applied to calculate weighted coefficients of key metrics. The optimal lrBW-IBI was observed through the sum of PCA weighted-metrics: the relative abundance of Latescibacteria (0.234), Gemmatimonadaceae (0.149), Nitrospira spp. (0.234), Rhizobiales (0.228), and nitrogenase NifH (0.156). According to PCA based lrBW-IBI, 12.12%, 24.24%, 39.39%, and 24.24% of river sections were labeled excellent, good, moderate, and relatively poor, respectively. The ecological status of the lower mainstream of the Yangtze River did not change significantly across seasons but declined gradually from upstream to downstream. This study provides a new assessment tool for the ecological health of large rivers and highlights the importance of microbial ecological index in river ecology.

A machine learning model to assess the ecosystem response to water policy measures in the Tagus River Basin (Spain)

Anthropogenic activities are seriously endangering the conservation of biodiversity worldwide, calling for urgent actions to mitigate their impact on ecosystems. We applied machine learning techniques to predict the response of freshwater ecosystems to multiple anthropogenic pressures, with the goal of informing the definition of water policy targets and management measures to recover and protect aquatic biodiversity. Random Forest and Gradient Boosted Regression Trees algorithms were used for the modelling of the biological indices of macroinvertebrates and diatoms in the Tagus river basin (Spain). Among the anthropogenic stressors considered as explanatory variables, the categories of land cover in the upstream catchment area and the nutrient concentrations showed the highest impact on biological communities. The model was then used to predict the biological response to different nutrient concentrations in river water, with the goal of exploring the effect of different regulatory thresholds on the ecosystem status. Specifically, we considered the maximum nutrient concentrations set by the Spanish legislation, as well as by the legislation of other European Union Member States. According to our model, the current nutrient thresholds in Spain ensure values of biological indices consistent with the good ecological status in only about 60% of the total number of water bodies. By applying more restrictive nutrient concentrations, the number of water bodies with biological indices in good status could increase by almost 40%. Moreover, coupling more restrictive nutrient thresholds with measures that improve the riparian habitat yields up to 85% of water bodies with biological indices in good status, thus proving to be a key approach to restore the status of the ecosystem.

Rwenzori Score (RS): A Benthic Macroinvertebrate Index for Biomonitoring Rivers and Streams in the Rwenzori Region, Uganda

The Rwenzori region in Uganda, a global biodiversity hotspot, is currently undergoing exponential economic and population growth, which puts continuous stress on its freshwater ecosystems. In Sub-Saharan Africa, biomonitoring campaigns using region-specific biotic indices is limited, particularly in Uganda. In this research, we present the Rwenzori Score (RS), a new macroinvertebrate-based biotic index developed to specifically assess the aquatic health of Rwenzori streams and rivers. We collected and measured both biological and physicochemical variables and identified 34,202 macroinvertebrates, belonging to 64 different taxa. The RS was developed in two steps. First, using canonical ordination, we identified chemical variables that correlated significantly with gradients in macroinvertebrate assemblage distribution and diversity. Second, based on selected variables and weighted averages, we determined specific family indicator values and assigned pollution tolerance values (varying from 1: tolerant; to 10: sensitive) to a family. Finally, we established four water quality classes: poor, fair, good, and excellent. The RS is highly correlated with the Average Score Per Taxon System (p < 0.05), a well-known and widely used biotic index. The RS has 5 unique taxa that are not included in other regional indices. In this regard, the development of the RS is a beneficial tool for tailor-made biomonitoring that can contribute to the sustainable development of the Rwenzori stream and river basins.

Amine-modified kaolinite clay preserved thyroid function and renal oxidative balance after sub-acute exposure in rats

OBJECTIVES

Kaolinite clay is an abundant natural resource in Nigeria with several industrial applications. Incidentally, the wide-scale use of kaolinite clay is hampered by its small surface area. The objective of this study was to assess the effects of amine-modified clay on electrolyte, thyroid, and kidney function markers.

METHODS

Modification of kaolinite clay with an amine functional group was achieved using surface grafting technique. Characterization with a scanning electron microscope and Brunauer-Emmett Teller surface area analyzer confirmed this modification. However, there is sparse information on the effect of amine-modified kaolinite clay on electrolyte homeostasis, thyroid, and renal function. Rats were administered amine-modified kaolinite clay at the doses of 1, 2, and 5 mg/kg body weight.

RESULTS

After 14 days of repeated-dose treatment, there were no significant changes in levels of albumin, uric acid, triiodothyronine, thyroxine, ratio of triiodothyronine to thyroxine, and relative kidney organ weight. Furthermore, there were no changes in the concentration of potassium, although amine-modified kaolinite clay significantly decreased sodium, calcium, and total cholesterol levels. Amine-modified kaolinite clay, at all treatment doses, also preserved the renal histoarchitecture and oxidative balance in rats.

CONCLUSIONS

This study reports on the effect of amine-modified kaolinite clay on renal markers and thyroid function, and further deepens our understanding of their biochemical action. This baseline data may boost the prospect of using amine-modified kaolinite clay in the treatment of contaminated water.

River restoration is prone to failure unless pre-optimized within a mechanistic ecological framework | Insights from a model-based case study

River restoration with the use of in-stream structures has been widely implemented to maintain/improve physical habitats. However, the response of aquatic biota has often been too weak to justify the high costs of restoration projects. The ecological effectiveness of river restoration has thus been much debated over claims that large-scale environmental drivers often overshadow the potential positive ecological effects of locally placed in-stream structures. In this study, we used a two-dimensional hydrodynamic-habitat model to evaluate the ecological effectiveness of habitat restoration with the use of in-stream structures in various water discharges, ranging from near-dry to environmental flows. The habitat suitability of benthic macroinvertebrates and of three cyprinid fish species was simulated for six restoration schemes and at four discharge scenarios, and was compared with a reference model, without in-stream structures. We found that the ecological response to habitat restoration varied by species and life stages, it strongly depended on the reach-scale flow conditions, it was often negative at near-environmental flows, and when positive, mostly at near-dry flows, it was too low to justify the high costs of river restoration. Flow variation was the major environmental driver that our local habitat restoration schemes attempted -but mostly failed-to fine-tune. We conclude that traditional river restoration, based on trial and error, will likely fail and should be ecologically pre-optimized before field implementation. Widespread use of in-stream structures for ecological restoration is not recommended. However, at near-dry flows, the response of all biotic elements except for macroinvertebrates, was positive. In combination with the small habitat-suitability differences observed among structure types and densities, we suggest that sparse/moderate in-stream structure placement can be used for cost-effective river restoration, but it will only be ecologically effective -thus justifying the high implementation costs-when linked to very specific purposes: (i) to conserve endangered species and (ii) to increase/improve habitat availability/suitability during dry periods, thus proactively preventing/reducing the current and future ecological impacts of climate change.

Shallow Groundwater Quality and Its Controlling Factors in the Su-Xi-Chang Region, Eastern China

Understanding factors influencing groundwater quality is critical to the development of best management practices at the large watershed scale. In this study, the shallow groundwater (10–20 m depth) in the Su-Xi-Chang region, eastern China, was investigated as part of a monitoring program from 2007 to 2008 to analyze the regional groundwater quality as well as the hydrogeochemical processes and their controlling factors. Conventional physicochemical water parameters (pH, turbidity, electrical conductivity, dissolved oxygen, total phosphorus), major cations (Na⁺, Ca²⁺, Mg²⁺ and NH₄⁺) and anions (Cl⁻, NO₃⁻ and SO₄²⁻) were measured. Hydrochemical methods and multivariate statistical methods were applied to analyze the hydrogeochemical signatures, origins, the similarities among the variables and to identify the main pollution sources in the groundwater. The results showed that (1) the concentrations of TDS (224.89–1086.70 mg/L) and turbidity (0.1–18.60 NTU) were higher than the class II groundwater quality standards in China and the WHO drinking water standards, (2) there were extremely high concentrations of ammonia (0.01–32.90 mg/L), with a mean value of 0.72 mg/L and (3) the nitrate concentrations (average value of 22.07 mg/L) exceeded the class III groundwater quality standards. The study also provided evidence that weathering, dissolution of carbonate, halite and silicate and cation exchange were the possible primary hydrogeochemical control mechanisms in the groundwater. The sources of ammonia, total phosphorus, sulfates and nitrates included rock–water interactions and anthropogenic activities. The groundwater administration of pollution sinks and sources, long-term legal frameworks and economic incentives should be improved to optimize watershed scale management in the context of rapid development in China.

Interactive impacts of climatic, hydrologic and anthropogenic activities on watershed health

The current study aimed to comprehensively assess the potential watershed health (WH) using an adapted reliability, resilience and vulnerability (R_elR_esV_ul) framework for the 24 sub-watersheds of Shazand, Markazi Province, Iran. Towards this goal, the appropriate criteria and acceptable corresponding thresholds were adapted to calculate the main WH indictors of reliability (R_el), resilience (R_es) and vulnerability (V_ul). Accordingly, the R_elR_esV_ul framework was conceptualized and customized for five criteria of standardized precipitation index (SPI), normalized difference vegetation index (NDVI), soil erosion, and low and high flow discharges. The effect sizes of used criteria and indicators on the Shazand WH status were also determined. Consequently, the status of R_el, R_es and V_ul indicators and integrated WH status were mapped for four years of 1986, 1998, 2008 and 2014 and for the whole watershed by developing an integrated watershed health index (IWHI). Finally, the change detection approach was applied to determine the trend of changes in IWHI during last three decades. The results approved the high variability in effectability of R_el, R_es and V_ul indicators and integrated health status of the Shazand Watershed from the selected criteria in study four years. The results revealed that in the all study years, the R_el indicator almost had the higher contribution rate (≥34%) in the Shazand WH. None of sub-watersheds in the study years were assessed in a healthy status in terms of IWHI based on the R_elR_esV_ul framework. In terms of WH change detection over the study periods, WH was found to declining by some 4% in the periods of 1986-1998 and 1986-2008 due to impact of industrialization and urban development. Whilst, the health status was non-significantly improved in other study periods. The present procedure can be supposed as a screening tool for a directive and efficient management of the watersheds.

Integrating river hydromorphology and water quality into ecological status modelling by artificial neural networks

The aim of the study was to develop predictive models of the ecological status of rivers by using artificial neural networks. The relationships between five macrophyte indices and the combined impact of water pollution as well as hydromorphological degradation were examined. The dataset consisted of hydromorphologically modified rivers representing a wide water quality gradient. Three ecological status indices, namely the Macrophyte Index for Rivers (MIR), the Macrophyte Biological Index for Rivers (IBMR) and the River Macrophyte Nutrient Index (RMNI), were tested. Moreover two diversity indices, species richness (N) and the Simpson index (D) were tested. Physico-chemical parameters reflecting both water quality and hydromorphological status were utilized as explanatory variables for the artificial neural networks. The best modelling quality in terms of high values of coefficients of determination and low values of the normalized root mean square error was obtained for the RMNI and the MIR networks. The networks constructed for IBMR, species richness and the Simpson index showed a lower degree of fit. In all cases, modelling quality improved by adding two hydromorphological indices to the pool of explanatory variables. The significant effect of these indices in the models was confirmed by sensitivity analysis. The research showed that ecological assessment of rivers based on macrophyte metrics does not only reflect the water quality but also the hydromorphological status.