WITHDRAWN: A preliminary assessment on spatial prioritization for integrated urban flood management based on source tracking method.

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Plant functional traits explain long-term differences in ecosystem services between artificial forests and natural grasslands.

Declining ecosystem services have prompted numerous studies aiming at developing more sustainable management practices for vegetation restoration. Advances in functional ecology indicate that the sustainable management of afforestation ecosystems should be performed based on plant functional traits, which provides pivotal knowledge for long-term sustainable vegetation restoration. Currently, the mechanism of how plant functional traits affect long term ecosystem services in restored areas is still unclear. This study investigates plant functional traits and the associated ecosystem services from artificial forestlands (Robinia pseudoacacia, Caragana korshinskii) and natural grasslands following different durations of vegetation restoration (10, 20, 30 and 40 years) in the Danangou watershed, a loess hilly-gully region in the Loess Plateau, China. The results showed that 1) the water conservation services of artificial forestlands first decreased and then increased over time, whereas the soil conservation service had an opposite trend; in turn, natural grassland led to a consistent increase in soil conservation and carbon sequestration services over time. 2) Artificial forestlands had greater soil conservation and carbon sequestration services than natural grassland but had lower water conservation services. 3) Leaves had a greater impact on carbon sequestration and water conservation services than did root length and root biomass density. 4) Root biomass density had a greater effect on soil conservation services than did leaf carbon content and soil organic matter. 5) Leaf carbon content, specific root length, and root biomass density had significant effects on the trade-off value between any two ecosystem services with increasing time after restoration of artificial forestland. 6) Specific leaf area had a greater effect on the trade-off values among the three services than did the other functional traits in the natural grassland. In arid ecosystems, natural grasslands are the best restoration strategy given their higher water conservation services. However, in soil erosion-affected areas, restoration through artificial forestlands is more appropriate. To mitigate the trade-offs between ecosystem services, it is recommended that artificial forestlands be thinned before the leaf carbon content, specific root length, and root biomass density reach a maximum (i.e., mature forestland).

Evaluating the relationship between groundwater quality and land use in an urbanized watershed.

Understanding the impact of urbanization on groundwater quality is critical. Effective water management requires understanding the relationship between land use and water quality. The study's goals were to compare the effects of land use, identify the types of land that impact hydrochemistry, and define how different land use affects water quality. For this purpose, the comparative relationship between groundwater quality, land use classes and landscape metrics were established for the years 2016 and 2021. Water samples were collected from 42 wells, and different hydro-chemical variables were considered to calculate the water quality index (WQI). The WQI value in 2016 ranged from 26.49 to 151.03 and 29.65 to 155.62 in 2021. The results indicate that the water quality in most parts of the study area is moderate for drinking and domestic purpose use. The google earth engine platform was used and radiometrically corrected and orthorectified Sentinel-2 satellite images were processed to classify land use classes for selected years. Five buffer zones were established within a 2-km watershed along each well site, and the effects of land use types and landscape metrics on water quality in the buffer zones were analyzed. Results revealed that the effects of land use types on water quality were mainly reflected in buffer 1 (B1), buffer 4 (B4), buffer 5 (B5) in 2016 and B1, buffer 3 (B3), and B5 in 2021. The impacts of landscape-level metrics on water quality are mainly reflected in buffer 2 (B2) and B3 in 2021, while at the class-level, they are mainly reflected in B1 and B4 in 2021. The redundancy analysis revealed that different hydro-chemical variables behaved differently with the land use classes and landscape metrics in the various buffer zones.

Community characteristics of macroinvertebrates and ecosystem health assessment in Qin River, a main tributary of the Yellow River in China.

To know well the ecosystem health status of Qin River, a main tributary of the Yellow River and the largest river in Jincheng region, macroinvertebrates from 49 sampling sites in the Qin River and its largest tributary, the Dan River, were investigated, and community characteristics were analyzed in the autumn of 2020; a Benthic index of Biotic Integrity (B-IBI) was established based on four metrics by a series of steps. The results showed that a total of 38 species of macroinvertebrates were collected and identified, belonging to 6 orders and 19 families, consisting of 17 Insecta species, 13 Gastropoda species, and 4 Oligochaeta species. Four species in Insecta belonged to EPT (E, Ephemeroptera; P, Plecoptera; T, Trichoptera); 10 species in Insecta belonged to Chironomidae and Tipulidae families. All species in Gastropoda belonged to Basematophora order, and, especially, Bellamya aeruginosa is highly tolerant to nutrients. All species in Oligochaetes belonged to Tubificidae family, which indicates eutrophication and low-dissolved oxygen. The dominant species in the study were Ephemera orientalis, Chironomus riparius Meigen, and Limnodrilus claparedianus. The final B-IBI scores varied from 0.75 to 3.75, with 5 sites in "excellent," 10 sites in "good," 10 sites in "normal" status, 12 sites in "poor" status, 12 sites in "very poor." "Very poor" and "poor" sites were mainly located in the middle reach of the Qin River and upper-middle reach of the Dan River in Jincheng region. B-IBI strongly differentiated the reference sites and impaired sites, suggesting the suitability of the B-IBI in the Qin River basin. Significantly negative correlations between NH4+-N, TN, and B-IBI indicated the B-IBI characterized well the influence of nitrogen pollution.

Joint Risk Analysis of Extreme Rainfall and High Tide Level Based on Extreme Value Theory in Coastal Area.

Extreme rainfall and high tide levels are the main causal factors of urban flood disasters in coastal areas. As complex interactions between these factors can exacerbate the impact of urban flood disasters in coastal areas, an associated flood risk assessment involves not only the estimation of the extreme values of each variable but also their probability of occurring simultaneously. With a consideration of the Shenzhen River Basin (China), this study used bivariate copula functions to quantitatively evaluate the joint risk of extreme rainfall and a high tide level. The results showed that a significant positive correlation exists between extreme rainfall and the corresponding high tide level, and that if the positive dependency was ignored, the probability of simultaneous extreme events would be underestimated. If a dangerous event is defined as one in which heavy rainfall and high tide level events occur concurrently, the "AND" joint return period based on the annual maxima method should be adopted. If a dangerous event is defined as one in which either only a heavy rainfall or a high tide level event occurs, the "OR" joint return period should be adopted. The results represent a theoretical basis and decision-making support for flood risk management and flood prevention/reduction in coastal areas.

Urban Flood Risk Assessment Based on Dynamic Population Distribution and Fuzzy Comprehensive Evaluation.

Floods are one of the most common natural disasters that can cause considerable economic damage and loss of life in many regions of the world. Urban flood risk assessment is important for urban flood control, disaster reduction, and risk management. In this study, a novel approach for assessing urban flood risk was proposed based on the dynamic population distribution, improved entropy weight method, fuzzy comprehensive evaluation method, and the principle of maximum membership, and the spatial distribution of flood risk in four different sessions or daily time segments (TS1-TS4) in the northern part of the Shenzhen River Basin (China) was assessed using geographic information system technology. Results indicated that risk levels varied with population movement. The areas of highest risk were largest in TS1 and TS3, accounting for 7.03% and 7.07% of the total area, respectively. The areas of higher risk were largest in TS2 and TS4, accounting for 4.54% and 4.64% of the total area, respectively. The findings of this study could provide a theoretical basis for assessing urban flood risk management measures in Shenzhen (and even throughout China), and a scientific basis for development of disaster prevention and reduction strategies by flood control departments.

Assessment of the impact of urban water system scheduling on urban flooding by using coupled hydrological and hydrodynamic model in Fuzhou City, China.

With climate change and urbanization development, urban areas are facing more serious floods. As a result, hydrological and hydrodynamic models have recently shown a broad application prospect in urban flood simulating and forecasting. For the area with rich inland rivers, urban water resources can be effectively regulated and redistributed through river networks and hydraulic structures scheduling. However, the lack of research on the effect of scheduling becomes a major limitation in model applications. Based on a coupled hydrodynamics model, the current study simulates the flooding response to the combined rainstorm and scheduling scenarios and analyzes the river overflow at the community scale. The result indicated that three local regions in the Jin'an study area are inundated easily. The locations near Qinting Lake were more sensitive to the water regulation rules than others. In the model of control on Qinting Lake, section A is more sensitive to the schedule control than section B, while for section A, the water level increased by 1.44% under the return period (RP) (10 a), and the rate changed to 2.64% under the RP (100 a). The differences in inundation from various scenarios are relatively small. In the mode of joint discharge rules under RP (50 a), the water level changed by 4.77% in section A and 1.24% in section B. The simulation at the community scale considers the overflow process, and the results indicated that the total inundation area decreased by 12.8 ha under joint schedules. The significant effects to alleviate urban inundation mainly come from the decreased flood overflow from the channel, but not from the flooding nodes. This study provides promising references for urban flood management.

Identification of priority management areas for non-point source pollution based on critical source areas in an agricultural watershed of Northeast China.

Identification of critical source areas (CSAs) for non-point source (NPS) pollution is of great significance for environment governance and prevention. However, the CSAs are generally characterized as great spatial dispersion, and spatially heterogeneous precipitation has a great influence on the spatial distribution of nutrient yields. Therefore, we identify the CSAs for nutrient yields in an agricultural watershed of Northeast China at hydrological response units (HRUs) scale based on the Soil and Water Assessment Tool (SWAT), assess the impacts of spatially heterogeneity of precipitation on the identification of the CSAs, analyze the sensitivity of nutrient yields to precipitation by scenarios analysis method, and further identify priority management areas (PMAs) that have poor ability to retain nutrients. The results showed that the CSAs for nutrient yields identified by uniform precipitation showed greater fluctuation range and coverage area than actual precipitation; the major prevention areas of total nitrogen (TN) yield were mainly distributed in regions nearby main stem of lower reaches, while that of total phosphorus (TP) yield were mostly located in urban area nearby outlet of the watershed; the identification of the PMAs significantly decreased the CSAs for TN yield, whereas that for TP yield was no significant difference with the CSAs. This study could provide scientific guidance for the NPS pollution governance and prevention.

Mapping of groundwater productivity potential with machine learning algorithms: A case study in the provincial capital of Baluchistan, Pakistan.

Although groundwater (GW) potential zoning can be beneficial for water management, it is currently lacking in several places around the world, including Pakistan's Quetta Valley. Due to ever increasing population growth and industrial development, GW is being used indiscriminately all over the world. Recognizing the importance of GW potential for sustainable growth, this study used to 16 GW drive factors to evaluate their effectiveness by using six machine learning algorithms (MLA's) that include artificial neural networks (ANN), random forest (RF), support vector machine (SVM), K- Nearest Neighbor (KNN), Naïve Bayes (NB) and Extreme Gradient Boosting (XGBoost). The GW yield data were collected and divided into 70% for training and 30% for validation. The training data of GW yields were integrated into the MLA's along with the GW driver variables and the projected results were checked using the Receiver Operating Characteristic (ROC) curve and the validation data. Out of six ML algorithms, ROC curve showed that the XGBoost, RF and ANN models performed well with 98.3%, 96.8% and 93.5% accuracy respectively. In addition, the accuracy of the models was evaluated using the mean absolute error (MAE), root mean square error (RMSE), F-score and correlation-coefficient. Hydro-chemical data were evaluated, and the water quality index (WQI) was also calculated. The final GW productivity potential (GWPP) maps were created using the MLA's output and WQI as they identify the different classification zones that can be used by the government and other agenciesto locate new GW wells and provide a basis for water management in rocky terrain.

Response of macroinvertebrate community to water quality factors and aquatic ecosystem health assessment in a typical river in Beijing, China.

Healthy aquatic ecosystems can offer basic ecological services for the sustainable development of humans and society. Water quality greatly influences the macroinvertebrate community in aquatic ecosystems and can alter the aquatic ecosystem's health status. However, the quantitative relationship between macroinvertebrate community and water quality factors in rivers remains unclear, particularly in urban rivers, which are strongly affected by human activities. Therefore, a new framework for the quantitative analysis between macroinvertebrate community and key water quality driving factors was developed in the study, meanwhile, the aquatic ecosystem health conditions were evaluated and validated by different methods. The framework was applied to a typical urban river, the North Canal River, which is regarded as the "mother river" of Beijing. Combined with the redundancy analysis (RDA) and the threshold indicator taxa analysis (TITAN), the water quality driving factors and their indicator species were identified and the corresponding response threshold was determined. Based on the benthic index of biotic integrity (B-IBI), the multi-metric rapid bioassessment method, and the biological monitoring working party (BMWP) score, the aquatic ecosystem health condition in the basin was comprehensively evaluated. The results show that fluoride, biochemical oxygen demand, ammonia-nitrogen and total phosphorus were the key water quality driving factors influencing the community structure of macroinvertebrates. Four indicator species of ammonia-nitrogen were identified by the TITAN method with a threshold range of 1.09-6.94 mg L-1, and three indicator species of total phosphorus were identified with a threshold range of 0.48-1.27 mg L-1. According to the results of the aquatic ecosystem health assessment, the river ecosystem was generally unhealthy and the upstream was better than downstream; the health condition in the mountainous areas of Changping district was the best, while that in Chaoyang district and the central city area was the worst. The framework could provide a strong basis for ecological restoration and pollution control of the urban rivers and become an important tool for the rehabilitation of aquatic ecosystems.

[Quantitative Analysis of the Correlation Between Macrobenthos Community and Water Environmental Factors and Aquatic Ecosystem Health Assessment in the North Canal River Basin of Beijing].

Macrobenthos can reflect the cumulative effect of various ecological threats on the water environment and are closely related to the health of river ecosystems. In this study, taking the North Canal River basin, a typical basin in Beijing, as an example, ecological data from 34 stations were investigated in the summer of 2015. Characteristics of the macrobenthos communities were analyzed, and driving environmental factors were identified using typical correspondence analysis. Thresholds and response species of those driving environmental factors were conducted using the thresholds indicator taxa analysis (TITAN). In this study, the health status of the river ecosystem was evaluated by the multi-metrics method and benthic index of biotic integrity (B-IBI). The benthic community was dominated by pollution-tolerant aquatic insects and mollusks, with a low-level Shannon-wiener diversity index between 0-1.01; fluoride, biochemical oxygen demand, ammonia-nitrogen, and total phosphorus were driving environmental factors influencing the community structure of macrobenthos. Indicator species of ammonia-nitrogen were identified by the TITAN in the North Canal River basin with a threshold range of 1.09-6.94 mg·L-1; three indicator species of total phosphorus were identified with a threshold range of 0.48-1.27 mg·L-1, which were all positive response species. According to the health assessment, the river ecosystem in the North Canal River basin was generally unhealthy, and the upstream ecosystem was better than that downstream; the health conditions in the mountainous areas of Changping district were the best, whereas those in Chaoyang and central city districts were the worst. This study can provide a basis for ecological restoration and pollution control of rivers and also provide a reference for the water ecological civilization construction in other cities.

Prediction of chemical reproductive toxicity to aquatic species using a machine learning model: An application in an ecological risk assessment of the Yangtze River, China.

The endocrine disrupting chemicals (EDCs) have been at the forefront of environmental issues for over 20 years and are a principle factor considered in every ecological risk assessment, but this kind of risk assessment faces difficulties. The expense, time cost of in vivo tests, and lack of toxicity data are key limiting factors for the ability to conduct ecological risk assessments of EDCs to aquatic species. In this study, a machine learning model named the support vector machine (SVM) was used to predict the reproductive toxicity of EDCs, and the performance of the models was evaluated. The results showed that the SVM model provided more accurate toxicity prediction data compared with the interspecies correlation estimation (ICE) model developed by previous study to predict the reproductive toxicity. The application of the predicted toxicity data was an important supplement to the observed data for the ecological risk assessment of EDCs in the Yangtze River, where estrogens and phenolic compounds have been found at some sampling sites in the middle and lower reaches. The results showed that the ecological risk of estrone, 17ß-estradiol, and ethinyl estradiol were significant. This study revealed the application potential of machine learning models for the prediction of reproductive toxicity effects of EDCs. This can provide reliable alternative toxicity data for the ecological risk assessments of EDCs.

Influences of Shifted Vegetation Phenology on Runoff Across a Hydroclimatic Gradient.

Climate warming has changed vegetation phenology, and the phenology-associated impacts on terrestrial water fluxes remain largely unquantified. The impacts are linked to plant adjustments and responses to climate change and can be different in different hydroclimatic regions. Based on remote sensing data and observed river runoff of hydrological station from six river basins across a hydroclimatic gradient from northeast to southwest in China, the relative contributions of the vegetation (including spring and autumn phenology, growing season length (GSL), and gross primary productivity) and climatic factors affecting the river runoffs over 1982-2015 were investigated by applying gray relational analysis (GRA). We found that the average GSLs in humid regions (190-241 days) were longer than that in semi-humid regions (186-192 days), and the average GSLs were consistently extended by 4.8-13.9 days in 1982-2015 period in six river basins. The extensions were mainly linked to the delayed autumn phenology in the humid regions and to advanced spring phenology in the semi-humid regions. Across all river basins, the GRA results showed that precipitation (r = 0.74) and soil moisture (r = 0.73) determine the river runoffs, and the vegetation factors (VFs) especially the vegetation phenology also affected the river runoffs (spring phenology: r = 0.66; GSL: r = 0.61; autumn phenology: r = 0.59), even larger than the contribution from temperature (r = 0.57), but its relative importance is climatic region-dependent. Interestingly, the spring phenology is the main VF in the humid region for runoffs reduction, while both spring and autumn growth phenology are the main VFs in the semi-humid region, because large autumn phenology delay and less water supply capacity in spring amplify the effect of advanced spring phenology. This article reveals diverse linkages between climatic and VFs, and runoff in different hydroclimatic regions, and provides insights that vegetation phenology influences the ecohydrology process largely depending on the local hydroclimatic conditions, which improve our understanding of terrestrial hydrological responses to climate change.

[Impact of Spatial Heterogeneity of Precipitation on the Area Change in Critical Source Area of Non-point Sources Pollution].

Aiming at non-point sources pollution in the agricultural areas with large topographic fluctuations and spatial differences in precipitation, a SWAT model was used to evaluate the spatial variations in the critical source areas (CSAs) of total nitrogen (TN) and total phosphorus (TP) under two precipitation scenarios, i.e., heterogeneous precipitation and uniform precipitation. A change in the CSAs identified based on the two precipitation scenarios during the study period were statistically calculated, and the relationship between the CSAs and precipitation variables was discussed. The study results showed that when the total precipitation was the same, the variation tendency of the identified CSAs for TN and TP under the two precipitation scenarios were similar, and very close for a few years. According to the results of the pair t test, the CSAs of TP were not affected by the spatial variation of precipitation, while the change in CSAs for TN was more significant under different precipitation scenarios, which is likely due to the difference in the physical properties of nitrogen and phosphorus. The correlation analysis between the CSAs of TN and TP with precipitation variables showed that the variation in the CSAs of TP was positively correlated with the precipitation variables in the same year, while the variation in the CSAs of TN was strongly related to the precipitation variables of the previous year. The results obtained in this study are of great significance for further exploring the impact of uncertainty of precipitation, which is an important driving factor, on the CSAs of non-point sources pollution and the governance of agricultural non-point sources pollution.

Assessment of urban flood susceptibility using semi-supervised machine learning model.

In order to identify flood-prone areas with limited flood inventories, a semi-supervised machine learning model-the weakly labeled support vector machine (WELLSVM)-is used to assess urban flood susceptibility in this study. A spatial database is collected from metropolitan areas in Beijing, including flood inventories from 2004 to 2014 and nine metrological, geographical, and anthropogenic explanatory factors. Urban flood susceptibility is mapped and compared using logistic regression, artificial neural networks, and a support vector machine. Model performances are evaluated using four evaluation indices (accuracy, precision, recall, and F-score) as well as the receiver operating characteristic curve. The results show that WELLSVM can better utilize the spatial information (unlabeled data), and it outperforms all comparison models. The high-quality WELLSVM flood susceptibility map is thus applicable to efficient urban flood management.

Priorization of River Restoration by Coupling Soil and Water Assessment Tool (SWAT) and Support Vector Machine (SVM) Models in the Taizi River Basin, Northern China.

Identifying priority zones for river restoration is important for biodiversity conservation and catchment management. However, limited data due to the difficulty of field collection has led to research to better understand the ecological status within a catchment and develop a targeted planning strategy for river restoration. To address this need, coupling hydrological and machine learning models were constructed to identify priority zones for river restoration based on a dataset of aquatic organisms (i.e., algae, macroinvertebrates, and fish) and physicochemical indicators that were collected from 130 sites in September 2014 in the Taizi River, northern China. A process-based model soil and water assessment tool (SWAT) was developed to model the temporal-spatial variations in environmental indicators. A support vector machine (SVM) model was applied to explore the relationships between aquatic organisms and environmental indicators. Biological indices among different hydrological periods were simulated by coupling SWAT and SVM models. Results indicated that aquatic biological indices and physicochemical indicators exhibited apparent temporal and spatial patterns, and those patterns were more evident in the upper reaches compared to the lower reaches. The ecological status of the Taizi River was better in the flood season than that in the dry season. Priority zones were identified for different hydrological seasons by setting the target values for ecological restoration based on biota organisms, and the results suggest that hydrological conditions significantly influenced restoration prioritization over other environmental parameters. Our approach could be applied in other seasonal river ecosystems to provide important preferences for river restoration.

[Health Assessment of the Stream Ecosystem in the North Canal River Basin, Beijing, China].

With increasing urbanization, the stream ecosystem in Beijing has faced great challenges. Phytoplankton, benthic macroinvertebrates, and water quality were investigated based on 25 sampling sites in the North Canal River basin in July 2015, and the quality of the habitat was assessed in situ. A total of 22 metrics, including aquatic organism, hydrology, water quality, and habitat, were calculated to be the candidate indicators. A principal component analysis (PCA) and correlation analysis were used to select the core metrics from the candidate indicators, and the weight of each core metric was estimated by using the entropy method. The integrated index of stream ecological health was constructed to assess the health condition of the North Canal River basin. The results of the PCA and correlation analysis showed that nine metrics were selected as the core metrics to construct the integrated index of stream ecological health, i. e., the Shannon-Wiener diversity index of phytoplankton and benthic macroinvertebrates, water temperature, BOD5, NH4+-N, F-, Zn, petroleum, and the qualitative habitat evaluation index (QHEI). According to the results of the health assessment, 12% of the sampling sites in the North Canal River basin were considered to be healthy (Ⅰ) or sub-healthy (Ⅱ), and more than half were poor (Ⅳ) or bad (Ⅴ). Therefore, the aquatic ecosystem in the North Canal River basin was generally unhealthy. The upstream was better than the midstream and downstream, where the spatial heterogeneity of the health condition was strong. The health condition in the Nansha River, the midstream of the Qinghe River, and the main stream of the Tonghui River were poor, while the upstream of the Liangshui River and the tributaries of the Wenyu River were good. In general, the condition of the stream ecosystem in the North Canal River basin was relatively complicated.

Mapping flood susceptibility in mountainous areas on a national scale in China.

Mountainous terrain covers nearly half of China and is susceptible to floods, which can lead to substantial losses of human life and property. Historical flooding records from government bulletins and newspapers, the only available information regarding floods that have occurred in some mountainous areas, are valuable for understanding flood disaster mechanisms in these regions. In this study, the flood susceptibility in mountainous regions in China was mapped based on historical flooding records from 1949 to 2000. A Random Forest (RF) model, which can handle large datasets through factor contribution analysis, was chosen to characterize the relationships between flooding occurrences and twelve geographic, meteorological, and hydrological explanatory factors. The results indicate that the RF model can effectively identify flood-prone areas and has advantages over artificial neural network (ANN) and support vector machine (SVM) methods. Among these explanatory factors, the geographic factors (elevation, longitude and drainage density) are the most important predictors of flooding in China's mountainous areas, whereas the hydrological factors (relative elevation and curve number) are the least important. Two independent datasets of historical flooding events from the Bulletin of Flood and Drought Disasters in China (2006-2014) alongside news reports and yearbooks (2008-2014) were collected and chosen to validate the capability of the RF model. The validation results confirm that the RF model can identify the flood susceptibility with satisfactory accuracy. This study proposes a preliminary flood susceptibility map of mountainous areas in China and provides a reference for predicting and mitigating potentially disastrous flooding events.

A systematic approach for watershed ecological restoration strategy making: An application in the Taizi River Basin in northern China.

Aiming to protect freshwater ecosystems, river ecological restoration has been brought into the research spotlight. However, it is challenging for decision makers to set appropriate objectives and select a combination of rehabilitation acts from numerous possible solutions to meet ecological, economic, and social demands. In this study, we developed a systematic approach to help make an optimal strategy for watershed restoration, which incorporated ecological security assessment and multi-objectives optimization (MOO) into the planning process to enhance restoration efficiency and effectiveness. The river ecological security status was evaluated by using a pressure-state-function-response (PSFR) assessment framework, and MOO was achieved by searching for the Pareto optimal solutions via Non-dominated Sorting Genetic Algorithm II (NSGA-II) to balance tradeoffs between different objectives. Further, we clustered the searched solutions into three types in terms of different optimized objective function values in order to provide insightful information for decision makers. The proposed method was applied in an example rehabilitation project in the Taizi River Basin in northern China. The MOO result in the Taizi River presented a set of Pareto optimal solutions that were classified into three types: I - high ecological improvement, high cost and high benefits solution; II - medial ecological improvement, medial cost and medial economic benefits solution; III - low ecological improvement, low cost and low economic benefits solution. The proposed systematic approach in our study can enhance the effectiveness of riverine ecological restoration project and could provide valuable reference for other ecological restoration planning.

Development of a multimetric index based on benthic macroinvertebrates for the assessment of urban stream health in Jinan City, China.

Assessment of the health of urban streams is an important theoretical and practical topic, which is related to the impacts of physiochemical processes, hydrological modifications, and the biological community. However, previous assessments of the urban water quality were predominantly conducted by measuring physical and chemical factors rather than biological monitoring. The purpose of this study was to develop an urban stream multimetric index (USMI) based on benthic macroinvertebrates to assess the health of aquatic ecosystem in Jinan City. Two hundred and eighty-eight samples were collected during two consecutive years (2014-2015) from 48 sites located within the city. Metrics related to the benthic macroinvertebrate richness, diversity, composition and abundance, and functional feeding groups were selected by using box-plots and the Kruskal-Wallis test. The final index derived from selected metrics was divided into five river quality classes (excellent, good, moderate, poor, and bad). A validation procedure using box-plots and the non-parametric Mann-Whitney U test showed that the USMI was useful to assess the health of urban streams.