Assessment of Spatiotemporal Varying Relationships Between Rainfall,Land Cover and Surface Water Area Using Geographically Weighted Regression

Traditional regression techniques such as ordinary least squares (OLS) are often unable to accurately model spatially varying data and may ignore or hide local variations in model coefficients. A relatively new technique, geographically weighted regression (GWR) has been shown to greatly improve model performance compared to OLS in terms of higher R ² and lower corrected Akaike information criterion (AIC_C). GWR models have the potential to improve reliabilities of the identified relationships by reducing spatial autocorrelations and by accounting for local variations and spatial non-stationarity between dependent and independent variables. In this study, GWR was used to examine the relationship between land cover, rainfall and surface water habitat in 149 sub-catchments in a predominately agricultural region covering 2.6 million ha in southeast Australia. The application of the GWR models revealed that the relationships between land cover, rainfall and surface water habitat display significant spatial non-stationarity. GWR showed improvements over analogous OLS models in terms of higher R ² and lower AIC_C. The increased explanatory power of GWR was confirmed by the results of an approximate likelihood ratio test, which showed statistically significant improvements over analogous OLS models. The models suggest that the amount of surface water area in the landscape is related to anthropogenic drainage practices enhancing runoff to facilitate intensive agriculture and increased plantation forestry. However, with some key variables not present in our analysis, the strength of this relationship could not be qualified. GWR techniques have the potential to serve as a useful tool for environmental research and management across a broad range of scales for the investigation of spatially varying relationships.     

Application of geographically weighted regression model to analysis of spatiotemporal varying relationships between groundwater quantity and land use changes (case study: Khanmirza Plain,Iran)

Understanding the spatiotemporal relationships between land use/cover changes (LUCC) and groundwater resources is necessary for effective and efficient land use management. In this paper, geographically weighted regression (GWR) and ordinary least squares (OLS) models have been expanded to analyze varying spatial relationships between groundwater quantity changes and LUCC for three periods: 1987–2000, 2000–2010, and 1987–2010 in the Khanmirza Plain of southwestern Iran. For this purpose, TM images were used to generate LUCC (rainfed, irrigated, meadow, and bare lands). Groundwater quantity variables, including groundwater level changes (GLC) and groundwater withdrawal differences (GWD), were gathered from piezometric and agricultural wells data. The analysis of spatial autocorrelation (Moran’s I and local indicators of spatial association ) demonstrated that GWR has a better ability to model spatially varying data with very minimal clustering of residuals. The results R ² and corrected Akaike’s Information Criterion parameters revealed that the GWR has the lowest similarity in space and time in neighboring situations and it has the high ability to explain more variance in the LUCC as a function of the groundwater quantity changes. All results of the distribution of local R ² values from GWR confirm our assertion that there is a spatiotemporal relationship between types of land use and each of groundwater quantity variables within the region. According to the t test results from GWR, there are significant differences between the GLC and GWD and the land use types in different places of region in each of the three time series. The GWR results can help decision-makers to make appropriate decisions for future planning.     

The relative influence of nutrients and habitat on stream metabolism in agricultural streams

Stream metabolism was measured in 33 streams across a gradient of nutrient concentrations in four agricultural areas of the USA to determine the relative influence of nutrient concentrations and habitat on primary production (GPP) and respiration (CR-24). In conjunction with the stream metabolism estimates, water quality and algal biomass samples were collected, as was an assessment of habitat in the sampling reach. When data for all study areas were combined, there were no statistically significant relations between gross primary production or community respiration and any of the independent variables. However, significant regression models were developed for three study areas for GPP (r ² = 0.79–0.91) and CR-24 (r ² = 0.76–0.77). Various forms of nutrients (total phosphorus and area-weighted total nitrogen loading) were significant for predicting GPP in two study areas, with habitat variables important in seven significant models. Important physical variables included light availability, precipitation, basin area, and in-stream habitat cover. Both benthic and seston chlorophyll were not found to be important explanatory variables in any of the models; however, benthic ash-free dry weight was important in two models for GPP.     

Physical integrity: the missing link in biological monitoring and TMDLs

The Clean Water Act mandates that the chemical, physical, and biological integrity of our nation’s waters be maintained and restored. Physical integrity has often been defined as physical habitat integrity, and as such, data collected during biological monitoring programs focus primarily on habitat quality. However, we argue that channel stability is a more appropriate measure of physical integrity and that channel stability is a foundational element of physical habitat integrity in low-gradient alluvial streams. We highlight assessment tools that could supplement stream assessments and the Total Maximum Daily Load stressor identification process: field surveys of bankfull cross-sections; longitudinal thalweg profiles; particle size distribution; and regionally calibrated, visual, stream stability assessments. Benefits of measuring channel stability include a more informed selection of reference or best attainable stream condition for an Index of Biotic Integrity, establishment of a baseline for monitoring changes in present and future condition, and indication of channel stability for investigations of chemical and biological impairments associated with sediment discontinuity and loss of habitat quality.     

Pilot-study on GIS-based risk modelling of a climate warming induced tertian malaria outbreak in Lower Saxony (Germany)

This project was designed to establish baseline aquatic biological community structure and physical habitat conditions in select wadeable streams within the California Central Valley. A secondary objective was to evaluate possible water quality differences between site types and seasons. Two agricultural and two urban streams were monitored in spring and fall for two consecutive years beginning in the fall of 2002. Bioassessment sampling was conducted according to modified US EPA methods. The study included physical habitat assessment, water and sediment chemical analysis and characterization of the benthic macroinvertebrate community at each site. Water samples were analyzed for selected organophosphate insecticides, pyrethroid insecticides and herbicides, while sediment samples were analyzed for pyrethroids only. All sites had substantial physical habitat and water quality impairments, and the absence of pollution intolerant macroinvertebrates and dominance of pollution tolerant macroinvertebrates were indications of biological impairment. Due to the limited amount of water quality and pesticide data collected, it was not possible to definitively demonstrate any cause and effect relationships between BMI community structure and water quality or pesticide concentrations. Though most physical habitat parameters were similar and EPA physical habitat scores revealed on no significant differences between urban and agricultural sites (P? = ?0.290), a significant difference was seen in substrate embeddedness (P? = ?0.020). Dominant taxon found at all sites were chironomids, amphipods, and oligochaetes. Benthic macroinvertebrate metrics were significantly different between both types of sites (P? = ?0.001) and seasons (P? = ?0.014). Chironomidae taxon and those of the functional feeding group scrapers were greater at urban sites, while those of the functional feeding group filterers were greater at agricultural sites. In addition, the metric groups Chironomidae, filterers, and predators were found in greater numbers in the spring than the fall.     

Visually determined stream mesohabitats influence benthic macroinvertebrate assessments in headwater streams

Mesohabitat components such as substrate and surface flow types are intimately related to benthic macroinvertebrate assemblages in streams. Visual assessments of the distribution of these components provide a means of evaluating physical habitat heterogeneity and aid biodiversity surveys and monitoring. We determined the degree to which stream site and visually assessed mesohabitat variables explain variability (i.e., beta-diversity) in the relative abundance and presence-absence of all macroinvertebrate families and of Ephemeroptera, Plecoptera, and Trichoptera (EPT) genera. We systematically sampled a wide variety of mesohabitat arrangements as they occured in stream sites. We also estimated how much of the explanation given by mesohabitat was associated with substrate or surface flow types. We performed variation partitioning to determine fractions of explained variance through use of partial redundancy analysis (pRDA). Mesohabitats and stream sites explained together from 23 to 32 % of the variation in the four analyses. Stream site explained 8–11 % of that variation, and mesohabitat variables explained 13–20 %. Surface flow types accounted for >60 % of the variation provided by the mesohabitat component. These patterns are in accordance with those obtained in previous studies that showed the predominance of environmental variables over spatial location in explaining macroinvertebrate distribution. We conclude that visually assessed mesohabitat components are important predictors of assemblage composition, explaining significant amounts of beta-diversity. Therefore, they are critical to consider in ecological and biodiversity assessments involving macroinvertebrates.     

Effects of geomorphology, habitat, and spatial location on fish assemblages in a watershed in Ohio, USA

In this paper, we evaluate relationships between in-stream habitat, water chemistry, spatial distribution within a predominantly agricultural Midwestern watershed and geomorphic features and fish assemblage attributes and abundances. Our specific objectives were to: (1) identify and quantify key environmental variables at reach and system wide (watershed) scales; and (2) evaluate the relative influence of those environmental factors in structuring and explaining fish assemblage attributes at reach scales to help prioritize stream monitoring efforts and better incorporate all factors that influence aquatic biology in watershed management programs. The original combined data set consisted of 31 variables measured at 32 sites, which was reduced to 9 variables through correlation and linear regression analysis: stream order, percent wooded riparian zone, drainage area, in-stream cover quality, substrate quality, gradient, cross-sectional area, width of the flood prone area, and average substrate size. Canonical correspondence analysis (CCA) and variance partitioning were used to relate environmental variables to fish species abundance and assemblage attributes. Fish assemblages and abundances were explained best by stream size, gradient, substrate size and quality, and percent wooded riparian zone. Further data are needed to investigate why water chemistry variables had insignificant relationships with IBI scores. Results suggest that more quantifiable variables and consideration of spatial location of a stream reach within a watershed system should be standard data incorporated into stream monitoring programs to identify impairments that, while biologically limiting, are not fully captured or elucidated using current bioassessment methods.     

Characterizing spatial structure of sediment E. coli populations to inform sampling design

Escherichia coli can persist in streambed sediments and influence water quality monitoring programs through their resuspension into overlying waters. This study examined the spatial patterns in E. coli concentration and population structure within streambed morphological features during baseflow and following stormflow to inform sampling strategies for representative characterization of E. coli populations within a stream reach. E. coli concentrations in bed sediments were significantly different (p?=?0.002) among monitoring sites during baseflow, and significant interactive effects (p?=?0.002) occurred among monitoring sites and morphological features following stormflow. Least absolute shrinkage and selection operator (LASSO) regression revealed that water velocity and effective particle size (D ₁₀) explained E. coli concentration during baseflow, whereas sediment organic carbon, water velocity and median particle diameter (D ₅₀) were important explanatory variables following stormflow. Principle Coordinate Analysis illustrated the site-scale differences in sediment E. coli populations between disconnected stream segments. Also, E. coli populations were similar among depositional features within a reach, but differed in relation to high velocity features (e.g., riffles). Canonical correspondence analysis resolved that E. coli population structure was primarily explained by spatial (26.9–31.7 %) over environmental variables (9.2–13.1 %). Spatial autocorrelation existed among monitoring sites and morphological features for both sampling events, and gradients in mean particle diameter and water velocity influenced E. coli population structure for the baseflow and stormflow sampling events, respectively. Representative characterization of streambed E. coli requires sampling of depositional and high velocity environments to accommodate strain selectivity among these features owing to sediment and water velocity heterogeneity.     

Land use impacts on river health of Uma Oya,Sri Lanka: implications of spatial scales

Human actions on landscapes are a principal threat to the ecological integrity of river ecosystems worldwide. Tropical landscapes have been poorly investigated in terms of the impact of catchment land cover alteration on water quality and biotic indices in comparison to temperate landscapes. Effects of land cover in the catchment at two spatial scales (catchment and site) on stream physical habitat quality, water quality, macroinvertebrate indices and community composition were evaluated for Uma Oya catchment in the upper Mahaweli watershed, Sri Lanka. The relationship between spatial arrangement of land cover in the catchment and water quality, macroinvertebrate indices and community composition was examined using univariate and multivariate approaches. Results indicate that chemical water quality variables such as conductivity and total dissolved solids are mostly governed by the land cover at broader spatial scales such as catchment scale. Shannon diversity index was also affected by catchment scale forest cover. In stream habitat features, nutrients such as N-NO₃ ^?, macroinvertebrate family richness, %shredders and macroinvertebrate community assemblages were predominantly influenced by the extent of land cover at 200 m site scale suggesting that local riparian forest cover is important in structuring macroinvertebrate communities. Thus, this study emphasizes the importance of services provided by forest cover at catchment and site scale in enhancing resilience of stream ecosystems to natural forces and human actions. Findings suggest that land cover disturbance effects on stream ecosystem health could be predicted when appropriate spatial arrangement of land cover is considered and has widespread application in the management of tropical river catchments.     

Review of the effects of in-stream pipeline crossing construction on aquatic ecosystems and examination of Canadian methodologies for impact assessment

Pipeline crossing construction alters river and stream channels, hence may have detrimental effects on aquatic ecosystems. This review examines the effects of crossing construction on fish and fish habitat in rivers and streams, and recommends an approach to monitoring and assessment of impacts associated with these activities. Pipeline crossing construction is shown to not only compromise the integrity of the physical and chemical nature of fish habitat, but also to affect biological habitat (e.g., benthic invertebrates and invertebrate drift), and fish behavior and physiology. Indicators of effect include: water quality (total suspended solids TSS), physical habitat (substrate particle size, channel morphology), benthic invertebrate community structure and drift (abundance, species composition, diversity, standing crop), and fish behavior and physiology (hierarchy, feeding, respiration rate, loss of equilibrium, blood hematocrit and leukocrit levels, heart rate and stroke volume). The Before-After-Control-Impact (BACI) approach, which is often applied in Environmental Effects Monitoring (EEM), is recommended as a basis for impact assessment, as is consideration of site-specific sensitivities, assessment of significance, and cumulative effects.     

Water quality time series for Big Melen stream (Turkey): its decomposition analysis and comparison to upstream

Big Melen stream is one of the major water resources providing 268 km³ year^???1 of drinking and municipal water for Istanbul. Monthly time series data between 1991 and 2004 for 25 chemical, biological, and physical water properties of Big Melen stream were separated into linear trend, seasonality, and error components using additive decomposition models. Water quality index (WQI) derived from 17 water quality variables were used to compare Aksu upstream and Big Melen downstream water quality. Twenty-six additive decomposition models of water quality time series data including WQI had R ² values ranging from 88% for log(water temperature) (P?≤?0.001) to 3% for log(total dissolved solids) (P?≤?0.026). Linear trend models revealed that total hardness, calcium concentration, and log(nitrite concentration) had the highest rate of increase over time. Tukey’s multiple comparison pointed to significant decreases in 17 water quality variables including WQI of Big Melen downstream relative to those of Aksu upstream (P?≤?0.001). Monitoring changes in water quality on the basis of watersheds through WQI and decomposition analysis of time series data paves the way for an adaptive management process of water resources that can be tailored in response to effectiveness and dynamics of management practices.     

Relationships of sedimentation and benthic macroinvertebrate assemblages in headwater streams using systematic longitudinal sampling at the reach scale

Investigating relationships of benthic invertebrates and sedimentation is challenging because fine sediments act as both natural habitat and potential pollutant at excessive levels. Determining benthic invertebrate sensitivity to sedimentation in forested headwater streams comprised of extreme spatial heterogeneity is even more challenging, especially when associated with a background of historical and intense watershed disturbances that contributed unknown amounts of fine sediments to stream channels. This scenario exists in the Chattahoochee National Forest where such historical timber harvests and contemporary land-uses associated with recreation have potentially affected the biological integrity of headwater streams. In this study, we investigated relationships of sedimentation and the macroinvertebrate assemblages among 14 headwater streams in the forest by assigning 30, 100-m reaches to low, medium, or high sedimentation categories. Only one of 17 assemblage metrics (percent clingers) varied significantly across these categories. This finding has important implications for biological assessments by showing streams impaired physically by sedimentation may not be impaired biologically, at least using traditional approaches. A subsequent multivariate cluster analysis and indicator species analysis were used to further investigate biological patterns independent of sedimentation categories. Evaluating the distribution of sedimentation categories among biological reach clusters showed both within-stream variability in reach-scale sedimentation and sedimentation categories generally variable within clusters, reflecting the overall physical heterogeneity of these headwater environments. Furthermore, relationships of individual sedimentation variables and metrics across the biological cluster groups were weak, suggesting these measures of sedimentation are poor predictors of macroinvertebrate assemblage structure when using a systematic longitudinal sampling design. Further investigations of invertebrate sensitivity to sedimentation may benefit from assessments of sedimentation impacts at different spatial scales, determining compromised physical habitat integrity of specific taxa and developing alternative streambed measures for quantifying sedimentation.     

长江上游洛碛河段采砂后鱼类生境现状研究

近年来,长江上游大量集中采砂造成河床地貌大幅改变,进而改变了该河段原有的水流条件.采砂可能会改变鱼类生境,但采砂后的河道鱼类生境现状尚不明确.分别在2019年1月以及2020年6月、12月对洛碛河段进行了3次水声学调查,探明了洛碛河段的鱼群生境现状.基于采砂前后实测地形,分析了洛碛河段采砂前后的地形、流速、水深等参数变...     

Spatial Modeling of Mean Annual Temperature in Iran: Comparing Cokriging and Geographically Weighted Regression

Mapping spatial distribution of climatological parameters with a good degree of accuracy is crucial in environmental modeling and planning. Nowadays, there are various models to estimate and predict spatial variables in an area but some such as cokriging and geographically weighted regression (GWR) have got more attention from experts in this field. The objectives of this study are to evaluate and compare GWR with ordinary cokriging (OCK) techniques for estimating the mean annual air temperature (MAT) of Iran using European Centre for Medium-Range Weather Forecasts (ECMWF) data and auxiliary variables (e.g., longitude, latitude and altitude). The MAT-gridded data for Iran was collected in pixels during the time interval of 1987–2015 from the ERA-Interim re-analysis version of ECMWF. Validation results indicate that cokriging model with latitude and altitude for estimating MAT has the lowest MAE (0.0155), MBE (0.00085), RMSE (0.0251), and the highest NS (0.9999) in relation to other cokriging methods. On the other hand, GWR with altitude has better results than those of GWR with other auxiliary variables because of its MAE (0.1271), MBE (0.0124), RMSE (0.1760), and NS (0.9969). By comparing two mentioned methods, cokriging with latitude and altitude has provided the best performance in relation to GWR for prediction of MAT in Iran. To obtain accurate estimation of the spatial distribution of MAT, local residuals were analyzed. Results concluded that residuals of the OCK model have high spatial adaptations between the observed and predicted MAT data compared to the GWR model. Hence, OCK was a relatively optimum method for the estimation of MAT compared with GWR.

     

Evaluation of land use and water quality in an agricultural watershed in the USA indicates multiple sources of bacterial impairment

Pathogens are the number one cause of impairments of assessed rivers and streams in the USA and pose a significant human health hazard. The Dry Run Creek Watershed in Northeast Iowa has been designated as impaired by the State of Iowa because of high levels of Escherichia coli bacteria. To investigate the nature of this impairment, land use and stream bank assessments were coupled with comprehensive water quality monitoring. Physical, chemical, and biological parameters were measured at 13 different sites in the watershed, including pH, temperature, conductivity, dissolved oxygen, turbidity, total Kjeldahl nitrogen, ammonia-N, nitrate?+?nitrite-N, total phosphorus, and E. coli. In addition, benthic macroinvertebrate communities were analyzed at seven sites, and optical brightener tests were performed late in the season. Results identified segments of the watershed that were more prominent contributors of E. coli, and correlations were observed between levels of E. coli and several chemical parameters, including ammonia-N, total Kjeldahl nitrogen, and total phosphorus. Interestingly, distinct sites emerged as more prominent contributors of these elements during rain vs. non-rain events, suggesting different types of sources. Both the amount of rainfall and the time elapsed between the rain event and the sampling influenced E. coli levels during wet weather conditions. Nitrate?+?nitrite-N displayed a unique response to rain events compared with the other parameters, suggesting a different delivery route. Analyses of benthic macroinvertebrate communities were consistent with pollution trends. Collectively, these data suggest distinct agriculturally related E. coli contributions, as well as specific areas and practices for water quality improvement strategies. This study can serve as a resource for evaluating agricultural watersheds that are impaired for bacteria.     

Optimizing the flow adjustment of constituent concentrations via LOESS for trend analysis

Trend analysis of stream constituent concentrations requires adjustment for exogenous variables like discharge because concentrations often have variable relations with flow. To remove the influence of flow on stream water quality data, an accurate characterization of the relationship between the constituent and streamflow is needed. One popular method, locally weighted regression (LOESS), provides an effective means for flow-adjusting concentrations. The LOESS fit can be tailored to the data via the smoothing parameter (f), so that the user can avoid overfitting or oversmoothing the data. However, it is a common practice to use a single f value when flow-adjusting water quality data for trend analysis. This study provides a robust, automated method for determining the optimal f value (f_opt) for each dataset via an iterative K-fold cross-validation procedure that minimizes prediction error in LOESS. The method is developed by analyzing datasets of seven different constituents across 17 sites (119 datasets total) from a stream monitoring program in northwest Arkansas (USA). We recommend using 10 iterations of 10-fold cross-validation (10?×?10 CV) in order to select f_opt when flow-adjusting water quality data with LOESS. The use of a default f value did not produce different trend interpretations for the data used here; however, the proposed approach may be helpful in other water quality studies which employ similar statistical fitting methods. Additionally, we provide an implementation of the method in the R statistical computing environment.     

Use of fuzzy logic models for prediction of taste and odor compounds in algal bloom-affected inland water bodies

Mechanistic modeling of how algal species produce metabolites (e.g., taste and odor compounds geosmin and 2-methyl isoborneol (2-MIB)) as a biological response is currently not well understood. However, water managers and water utilities using these reservoirs often need methods for predicting metabolite production, so that appropriate water treatment procedures can be implemented. In this research, a heuristic approach using Adaptive Network-based Fuzzy Inference System (ANFIS) was developed to determine the underlying nonlinear and uncertain quantitative relationship between observed cyanobacterial metabolites (2-MIB and geosmin), various algal species, and physical and chemical variables. The model is proposed to be used in conjunction with numerical water quality models that can predict spatial–temporal distribution of flows, velocities, water quality parameters, and algal functional groups. The coupling of the proposed metabolite model with the numerical water quality models would assist various utilities which use mechanistic water quality models to also be able to predict distribution of taste and odor metabolites, especially when monitoring of metabolites is limited. The proposed metabolite model was developed and tested for the Eagle Creek Reservoir in Indiana (USA) using observations over a 3-year period (2008–2010). Results show that the developed models performed well for geosmin (R ²?=?0.83 for all training data and R ²?=?0.78 for validation of all 10 data points in the validation dataset) and reasonably well for the 2-MIB (R ²?=?0.82 for all training data and R ²?=?0.70 for 7 out of 10 data points in the validation dataset).     

Linking biological and physicochemical water quality

To define water quality, the European Water Framework Directive (WFD) demands complex assessments through physicochemical, biological, and hydromorphological controls of water bodies. Since the biological assessment became the central focus with hydrochemistry playing a supporting role, an evaluation of the interrelationships within this approach deems necessary. This work identified and tested these relationships to help improve the quality and efficiency of related efforts. Data from the 384 km² Weisseritz catchment (eastern Erzgebirge, Saxony, Germany and northern Bohemia, Czech Republic) were used as a representative example for central European streams in mountainous areas. The data cover the time frame 1992 to 2003. To implement WFD demands, the analysis was based on accepted German methods and classifications, WFD quality standards, and novel German methods for the biological status assessment. Selected chemical parameters were compared with different versions of the German Saprobic Index, based on macroinvertebrate indicator taxa. Relevant dependencies applicable for integrated stream assessment were statistically tested. Correlation analysis showed significant relationships. The highest scores were found for nutrients (NO₂ ^???, N_inorg, and total N), salinity (Cl^???, SO₄ ^2???, conductivity), and microelements (K^?+?, Na^?+?, Ca^2?+?, Mg^2?+?). The Saprobic Index used in the Integrated Assessment System for the Ecological Quality of Streams and Rivers throughout Europe using Benthic Macro-invertebrates program seems to be the most sensitive indicator to correlate with chemical parameters.