Enhancing perspectives on lake impairments using satellite observations: A case study on High Rock Lake,North Carolina

Stakeholders developing water quality improvement plans for lakes and reservoirs are challenged by the sparsity of in-situ data and the uncertainty ingrained in management decisions. This study explores how satellite images can fill gaps in water quality databases and provide more holistic assessments of impairments. The study site is an impaired water body that is serving as a pilot for improving state-wide nutrient management planning processes. An existing in-situ database was used to calibrate semi-analytical models that relate satellite reflectance values to turbidity and total suspended solids (TSS). Landsat-7 images from 1999 to 2020 that overpass High Rock Lake, North Carolina were downloaded and processed, providing 42 turbidity and 39 TSS satellite and in-situ match-ups for model calibration and validation. Model r-squared values for the fitted turbidity and TSS models are 0.72 and 0.74, and the mean absolute errors are 14.6 NTU and 3.2 mg/L. The satellite estimates were compared to the in-situ data and simulated TSS values produced by a calibrated hydrologic-hydrodynamic model. The process-based model is considered less accurate than the satellite model based on statistical performance metrics. Comparisons between data sources are illustrated with time series plots, frequency curves, and aggregate decision metrics to highlight the dependence of lake impairment assessments on the spatial and temporal frequency of available data and model accuracy.     

Application of best subset method for river water quality modeling: A study on Godavari River,India

The utilization of water quality analysis to inform optimal decision-making is imperative to achieve sustainable management of river water quality. A multitude of research works in the past has focused on river water quality modeling. Despite being a precise statistical regression technique that allows for fitting separate models for all potential combinations of predictors and selecting the optimal subset model, the application of best subset method in river water quality modeling is not widely adopted. The current research aims to validate the use of best subset method in evaluating the water quality parameters of the Godavari River, one of the largest rivers in India, by developing regression equations for different combinations of its physicochemical parameters. The study involves in formulating best subset regression equations to estimate the concentrations of river water quality parameters while also identifying and quantifying their variations. A total of 17 water quality parameters are analyzed at 13 monitoring sites using 13 years (1993–2005) of observed data for the monsoon (June–October) period and post-monsoon (November–February) period. The final subset model is selected among model combinations that are developed for each year's dataset through widely used statistical criteria such as R², F value, adjusted R²_a, AIC_c, and RSS. The final best subset model across all parameters exhibits R² values surpassing 0.8, indicating that the models possess the ability to account for over 80% of the variations in the concentrations of dependent parameters. Therefore, the findings demonstrated the appropriateness of this method in evaluating the water quality parameters in extensive rivers. This work is very useful for decision-making and in the management of river water quality for its sustainable use in the study area.     

Climatic Fluctuations and Forecasting of Streamflow in the Lower Colorado River Basin1

Abstract:  Water‐resource managers need to forecast streamflow in the Lower Colorado River Basin to plan for water‐resource projects and to operate reservoirs for water supply. Statistical forecasts of streamflow based on historical records of streamflow can be useful, but statistical assumptions, such as stationarity of flows, need to be evaluated. This study evaluated the relation between climatic fluctuations and stationarity and developed regression equations to forecast streamflow by using climatic fluctuations as explanatory variables. Climatic fluctuations were represented by the Atlantic Multidecadal Oscillation (AMO), Pacific Decadal Oscillation (PDO), and Southern Oscillation Index (SOI). Historical streamflow within the 25‐ to 30‐year positive or negative phases of AMO or PDO was generally stationary. Monotonic trends in annual mean flows were tested at the 21 sites evaluated in this study; 76% of the sites had no significant trends within phases of AMO and 86% of the sites had no significant trends within phases of PDO. As climatic phases shifted in signs, however, many sites had nonstationary flows; 67% of the sites had significant changes in annual mean flow as AMO shifted in signs. The regression equations developed in this study to forecast streamflow incorporate these shifts in climate and streamflow, thus that source of nonstationarity is accounted for. The R² value of regression equations that forecast individual years of annual flow for the central part of the study area ranged from 0.28 to 0.49 and averaged 0.39. AMO was the most significant variable, and a combination of indices from both the Atlantic and Pacific Oceans explained much more variation in flows than only the Pacific Ocean indices. The average R² value for equations with PDO and SOI was 0.15.     

Air‐Water Temperature Relationships in the Trout Streams of Southeastern Minnesota's Carbonate‐Sandstone Landscape

Carbonate‐sandstone geology in southeastern Minnesota creates a heterogeneous landscape of springs, seeps, and sinkholes that supply groundwater into streams. Air temperatures are effective predictors of water temperature in surface‐water dominated streams. However, no published work investigates the relationship between air and water temperatures in groundwater‐fed streams (GWFS) across watersheds. We used simple linear regressions to examine weekly air‐water temperature relationships for 40 GWFS in southeastern Minnesota. A 40‐stream, composite linear regression model has a slope of 0.38, an intercept of 6.63, and R² of 0.83. The regression models for GWFS have lower slopes and higher intercepts in comparison to surface‐water dominated streams. Regression models for streams with high R² values offer promise for use as predictive tools for future climate conditions. Climate change is expected to alter the thermal regime of groundwater‐fed systems, but will do so at a slower rate than surface‐water dominated systems. A regression model of intercept vs. slope can be used to identify streams for which water temperatures are more meteorologically than groundwater controlled, and thus more vulnerable to climate change. Such relationships can be used to guide restoration vs. management strategies to protect trout streams.     

A PRELIMINARY QUANTIFICATION OF THE IMPACTS OF ASPEN TO CONIFER SUCCESSION ON WATER YIELD - I. HEAT PULSE METHODOLOGY FOR MODEL CALIBRATION1

ABSTRACT: Twenty-six aspen (Populus tremuloides Michx.), 20 subalpine fir (Abies lasiocarps (Hook.) Nutt.), and 20 Engelmann spruce (Pices engelmanil (Parry) Engelm.) of various sizes were cut under water and suspended in permanent reserviors at a northern Utah site. The reservoirs were asealed so that all water loss was due to consumption by the trees. Sap velocities, as computed from heat pulse velocities, were related to conducting areas of the tree trunks. Computed transpiration volumes were then correlated with actual water losses from the reservoirs. Coefficients of determination (R²) of 0.87, 0.86, and 0.82 were obtained for the fir, aspen, and sprucs, respectively. Reservoir water loss for each species for each season was then used to adjust a plant activity index for computing transpiration within ASPCON, a model describing the hydrology of aspen to conifer succession. The plant activity index reflects the variation in the capability of a plant community to transpire water over the year. Assumptions and limitations of the heat pulse velocity technique are also outlined.     

Estimation of Suspended‐Sediment Concentration From Total Suspended Solids and Turbidity Data for Kentucky, 1978‐19951

Williamson, Tanja N. and Charles G. Crawford, 2011. Estimation of Suspended‐Sediment Concentration From Total Suspended Solids and Turbidity Data for Kentucky, 1978‐1995. Journal of the American Water Resources Association (JAWRA) 47(4):739‐749. DOI: 10.1111/j.1752‐1688.2011.00538.x Abstract: Suspended sediment is a constituent of water quality that is monitored because of concerns about accelerated erosion, nonpoint contamination of water resources, and degradation of aquatic environments. In order to quantify the relationship among different sediment parameters for Kentucky streams, long‐term records were obtained from the National Water Information System of the U.S. Geological Survey. Suspended‐sediment concentration (SSC), the parameter traditionally measured and reported by the U.S. Geological Survey, was statistically compared to turbidity and total suspended solids (TSS), two parameters that are considered surrogate data. A linear regression of log‐transformed observations was used to estimate SSC from TSS; 72% of TSS observations were less than coincident SSC observations; however, the estimated SSC values were almost as likely to be overestimated as underestimated. The SSC‐turbidity relationship also used log‐transformed observations, but required a nonlinear, breakpoint regression that separated turbidity observations ≤6 nephelometric turbidity units. The slope for these low turbidity values was not significantly different than zero, indicating that low turbidity observations provide no real information about SSC; in the case of the Kentucky sediment record, this accounts for 30% of the turbidity observations.     

Using Regression Tree Analysis to Improve Predictions of Low-Flow Nitrate and Chloride in Willamette River Basin Watersheds

The use of regression tree analysis is examined as a tool to evaluate hydrologic and land use factors that affect nitrate and chloride stream concentrations during low-flow conditions. Although this data mining technique has been used to assess a range of ecological parameters, it has not previously been used for stream water quality analysis. Regression tree analysis was conducted on nitrate and chloride data from 71 watersheds in the Willamette River Basin to determine whether this method provides a greater predictive ability compared to standard multiple linear regression, and to elucidate the potential roles of controlling mechanisms. Metrics used in the models included a variety of watershed-scale landscape indices and land use classifications. Regression tree analysis significantly enhanced model accuracy over multiple linear regression, increasing nitrate R ² values from 0.38 to 0.75 and chloride R ² values from 0.64 to 0.85 and as indicated by the ΔAIC value. These improvements are primarily attributed to the ability for regression trees to more effectively handle interactions and manage non-linear functions associated with watershed heterogeneity within the basin. Whereas hydrologic factors governed the conservative chloride tracer in the model, land use dominated control of nitrate concentrations. Watersheds containing higher agricultural activity did not necessarily yield high nitrate concentrations, but agricultural areas combined with either small proportions of forested land or greater urbanization generated nitrate levels far exceeding water quality standards. Although further refinements are recommended, we conclude that regression tree analysis presents water resource managers a promising tool that improves on the predictive ability of standard statistical methods, provides insight into controlling mechanisms, and helps identify catchment characteristics associated with water quality impairment.     

Integrating Satellite Imagery with Simulation Modeling to Improve Burn Severity Mapping

Both satellite imagery and spatial fire effects models are valuable tools for generating burn severity maps that are useful to fire scientists and resource managers. The purpose of this study was to test a new mapping approach that integrates imagery and modeling to create more accurate burn severity maps. We developed and assessed a statistical model that combines the Relative differenced Normalized Burn Ratio, a satellite image-based change detection procedure commonly used to map burn severity, with output from the Fire Hazard and Risk Model, a simulation model that estimates fire effects at a landscape scale. Using 285 Composite Burn Index (CBI) plots in Washington and Montana as ground reference, we found that an integrated model explained more variability in CBI (R ² = 0.47) and had lower mean squared error (MSE = 0.28) than image (R ² = 0.42 and MSE = 0.30) or simulation-based models (R ² = 0.07 and MSE = 0.49) alone. Overall map accuracy was also highest for maps created with the Integrated Model (63 %). We suspect that Simulation Model performance would greatly improve with higher quality and more accurate spatial input data. Results of this study indicate the potential benefit of combining satellite image-based methods with a fire effects simulation model to create improved burn severity maps.     

Estimating Sediment and Nutrient Loads in Four Western Lake Superior Streams

Total suspended solids (TSS) and total phosphorus (TP) have been shown to be strongly correlated with turbidity in watersheds. High‐frequency in situ turbidity can provide estimates of these potential pollutants over a wide range of hydrologic conditions. Concentrations and loads were estimated in four western Lake Superior trout streams from 2005 to 2010 using regression models relating continuous turbidity data to grab sample measures of TSS and TP during differing flow regimes. TSS loads estimated using the turbidity surrogate were compared with those made using FLUX software, a standard assessment technique based on discharge and grab sampling for TSS. More traditional rating curve methodology was not suitable because of the high variability in the particulates vs. discharge relationship. Stream‐specific turbidity and TSS data were strongly correlated (r² = 0.5 to 0.8; p < 0.05) and less so for TP (r² = 0.3 to 0.7; p < 0.05). Near‐continuous turbidity monitoring (every 15 min) provided a good method for estimating both TSS and TP concentration, providing information when manual sample collection was unlikely, and allowing for detailed analyses of short‐term responses of flashy Lake Superior tributaries to highly variable weather and hydrologic conditions while the FLUX model typically resulted in load estimates greater than those determined using the turbidity surrogate, with 17/23 stream years having greater FLUX estimates for TSS and 18/23 for TP.     

Designing and Implementing a Network for Sensing Water Quality and Hydrology across Mountain to Urban Transitions

Water resources are increasingly impacted by growing human populations, land use, and climate changes, and complex interactions among biophysical processes. In an effort to better understand these factors in semiarid northern Utah, United States, we created a real‐time observatory consisting of sensors deployed at aquatic and terrestrial stations to monitor water quality, water inputs, and outputs along mountain to urban gradients. The Gradients Along Mountain to Urban Transitions (GAMUT) monitoring network spans three watersheds with similar climates and streams fed by mountain winter‐derived precipitation, but that differ in urbanization level, land use, and biophysical characteristics. The aquatic monitoring stations in the GAMUT network include sensors to measure chemical (dissolved oxygen, specific conductance, pH, nitrate, and dissolved organic matter), physical (stage, temperature, and turbidity), and biological components (chlorophyll‐a and phycocyanin). We present the logistics of designing, implementing, and maintaining the network; quality assurance and control of numerous, large datasets; and data acquisition, dissemination, and visualization. Data from GAMUT reveal spatial differences in water quality due to urbanization and built infrastructure; capture rapid temporal changes in water quality due to anthropogenic activity; and identify changes in biological structure, each of which are demonstrated via case study datasets.     

Generation of Ensemble Streamflow Forecasts Using an Enhanced Version of the Snowmelt Runoff Model1

Harshburger, Brian J., Von P. Walden, Karen S. Humes, Brandon C. Moore, Troy R. Blandford, and Albert Rango, 2012. Generation of Ensemble Streamflow Forecasts Using an Enhanced Version of the Snowmelt Runoff Model. Journal of the American Water Resources Association (JAWRA) 48(4): 643‐655. DOI: 10.1111/j.1752‐1688.2012.00642.x Abstract: As water demand increases in the western United States, so does the need for accurate streamflow forecasts. We describe a method for generating ensemble streamflow forecasts (1‐15 days) using an enhanced version of the snowmelt runoff model (SRM). Forecasts are produced for three snowmelt‐dominated basins in Idaho. Model inputs are derived from meteorological forecasts, snow cover imagery, and surface observations from Snowpack Telemetry stations. The model performed well at lead times up to 7 days, but has significant predictability out to 15 days. The timing of peak flow and the streamflow volume are captured well by the model, but the peak‐flow value is typically low. The model performance was assessed by computing the coefficient of determination (R²), percentage of volume difference (Dv%), and a skill score that quantifies the usefulness of the forecasts relative to climatology. The average R² value for the mean ensemble is >0.8 for all three basins for lead times up to seven days. The Dv% is fairly unbiased (within ±10%) out to seven days in two of the basins, but the model underpredicts Dv% in the third. The average skill scores for all basins are >0.6 for lead times up to seven days, indicating that the ensemble model outperforms climatology. These results validate the usefulness of the ensemble forecasting approach for basins of this type, suggesting that the ensemble version of SRM might be applied successfully to other basins in the Intermountain West.     

RIDGE REGRESSION TECHNIQUES APPLIED TO LANDSAT INVESTIGATION OF WATER QUALITY IN LAKE OKEECHOBEE1

ABSTRACT: Landsat radiance values were processed at two different (single and double) levels of accuracy to estimate chlorophyll a, turbidity, and suspended sediment in Lake Okeechobee, Florida. Both ordinary least square and ridge regression analyses were used to establish a relationship between water quality parameters and Landsat radiance. Radiance measurements made at greater precision (double level) gave a better solution in this application. The ridge regression analysis for double level not only can reduce the total mean square error about 13–20 percent and confidence interval about 6–28 percent as compared to ordinary least square analysis, but it can also change the interpretation of analysis results.     

Surrogate Measures for Providing High Frequency Estimates of Total Suspended Solids and Total Phosphorus Concentrations1

Spackman Jones, Amber, David K. Stevens, Jeffery S. Horsburgh, and Nancy O. Mesner, 2010. Surrogate Measures for Providing High Frequency Estimates of Total Suspended Solids and Total Phosphorus Concentrations. Journal of the American Water Resources Association (JAWRA) 1‐15. DOI: 10.1111/j.1752‐1688.2010.00505.x Abstract: Surrogate measures like turbidity, which can be observed with high frequency in situ, have potential for generating high frequency estimates of total suspended solids (TSS) and total phosphorus (TP) concentrations. In the semiarid, snowmelt‐driven, and irrigation‐regulated Little Bear River watershed of northern Utah, high frequency in situ water quality measurements were recorded in conjunction with periodic chemistry sampling. Site‐specific relationships were developed using turbidity as a surrogate for TP and TSS at two monitoring locations. Methods are presented for employing censored data and for investigating categorical explanatory variables (e.g., hydrologic conditions). Turbidity was a significant explanatory variable for TP and TSS at both sites, which differ in hydrologic and water quality characteristics. The relationship between turbidity and TP was stronger at the upper watershed site where TP is predominantly particulate. At both sites, the relationships between turbidity and TP varied between spring snowmelt and base flow conditions while the relationships between TSS and turbidity were consistent across hydrological conditions. This approach enables the calculation of high frequency time series of TP and TSS concentrations previously unavailable using traditional monitoring approaches. These methods have broad application for situations that require accurate characterization of fluxes of these constituents over a range of hydrologic conditions.     

CORRELATION OF SELECTED WELL WATER QUALITY PARAMETERS WITH SOIL AND AQUIFER HYDROLOGIC PROPERTIES1

ABSTRACT: The geographical distribution of well water specific electrical conductivity and nitrate levels in a 932 km² ground water quality study area in the Fresno-Clovis, California, indicated that frequently areas of lower ground water salinity were also areas of relatively greater soil and aquifer permeability. From these observations and certain assumptions we hypothesized that the quality of the well water should be better in areas with permeable soils and geological formations. Correlation and multiple linear regression analysis supported this hypothesis for well water salinity. However, well water nitrate levels were significantly negatively correlated with only the estimated equivalent specific yield of the aquifer system. The multiple R² values of the most significant multiple linear regression models showed that only a fourth to a third of the variability in well water specific electric conductivity and nitrate levels could be ascribed to the effects of the hydrogeological parameters considered with more than 90 percent confidence. This indicates that three-fourths to two-thirds of the variability in ground water salinity and nitrate levels may be related to land use. Thus, there is considerable room for land use management techniques to improve ground water quality and reduce its variability.     

Assessment of chlorophyll-a as a criterion for establishing nutrient standards in the streams and rivers of Illinois

Nutrient enrichment is a frequently cited cause for biotic impairment of streams and rivers in the USA. Efforts are underway to develop nutrient standards in many states, but defensible nutrient standards require an empirical relationship between nitrogen (N) or phosphorus (P) concentrations and some criterion that relates nutrient levels to the attainment of designated uses. Algal biomass, measured as chlorophyll-a (chl-a), is a commonly proposed criterion, yet nutrient-chl-a relationships have not been well documented in Illinois at a state-wide scale. We used state-wide surveys of >100 stream and river sites to assess the applicability of chl-a as a criterion for establishing nutrient standards for Illinois. Among all sites, the median total P and total N concentrations were 0.185 and 5.6 mg L(-1), respectively, during high-discharge conditions. During low-discharge conditions, median total P concentration was 0.168 mg L(-1), with 25% of sites having a total P of > or =0.326 mg L(-1). Across the state, 90% of the sites had sestonic chl-a values of < or =35 microg L(-1), and watershed area was the best predictor of sestonic chl-a. During low discharge there was a significant correlation between sestonic chl-a and total P for those sites that had canopy cover < or =25% and total P of < or =0.2 mg L(-1). Results suggest sestonic chl-a may be an appropriate criterion for the larger rivers in Illinois but is inappropriate for small rivers and streams. Coarse substrate to support benthic chl-a occurred in <50% of the sites we examined; a study using artificial substrates did not reveal a relationship between chl-a accrual and N or P concentrations. For many streams and rivers in Illinois, nutrients may not be the limiting factor for algal biomass due to the generally high nutrient concentrations and the effects of other factors, such as substrate conditions and turbidity.     

Water-Quality Monitoring and Biological Integrity Assessment in the Indian River Lagoon, Florida: Status, Trends, and Loadings (1988–1994)

a production in the central, south central, and the south segments of the lagoon. In a system as large and complex as the lagoon, N and P limitations are potentially subject to significant spatial and temporal variability. Total Kjeldahl nitrogen (TN) was higher in the north (1.25 mg/liter) and lower in the south (0.89 mg/liter). The reverse pattern was observed for total P (TP), i.e., lowest in the north (0.03 mg/liter) and highest at the south (0.14 mg/liter) ends of the IRL. This increased P concentration in the SIRL appears to have a significantly large effect on chlorophyll a production compared with the other segments, as indicated by stepwise regression statistics. This relationship can be expressed as follows: South IRL [chlorophyll a] =−8.52 + 162.41 [orthophosphate] + 7.86 [total nitrogen] + 0.38 [turbidity]; R ²= 0.98**.     

WASTE LOADINGS FROM A FRESHWATER ATLANTIC SALMON FARM IN SCOTLAND1

ABSTRACT: Recent studies suggest that waste generation from the freshwater phase of Atlantic salmon (Salmo salar L.) production varies considerably on an annual basis. A fish farm on the West Coast of Scotland was visited regularly during a two-year period to determine inflow and outflow water quality. Waste output budgets of suspended solids (SS), biochemical oxygen demand (BOD), total nitrogen (TN), total phosphorus (TP), total ammonia nitrogen (TAN = NH₃+NH₄⁺), dissolved reactive phosphorus (DRP) and total phosphorus (TP) were produced. The annual waste loadings obtained were 71 kg TN t fish^?1 yr^?1 (one year of data only), 10.9–11.1 kg TP t fish^?1 yr^?1, 1.2–2.1 kg DRP t fish^?1 yr^?1, 422–485 kg BOD₅ t fish^?1 yr^?1, 327–337 kg SS t fish^?1 yr^?1, and 30–35 kg TAN-N t fish^?1 yr^?1. Simple linear regression models relating waste parameter production to water temperature and feeding regime were developed. When compared to existing data for other salmonid production systems, greater ranges of daily waste loadings were observed. Wide variations in concentrations of these parameters during a daily cycle were also observed, suggesting that mass balance estimates of waste production will provide more robust estimates of waste output than frequent monitoring of outflow water quality.     

Comparison of Stream Invertebrate Response Models for Bioassessment Metrics1

Waite, Ian R., Jonathan G. Kennen, Jason T. May, Larry R. Brown, Thomas F. Cuffney, Kimberly A. Jones, and James L. Orlando, 2012. Comparison of Stream Invertebrate Response Models for Bioassessment Metrics. Journal of the American Water Resources Association (JAWRA) 48(3): 570-583. DOI: 10.1111/j.1752-1688.2011.00632.x Abstract: We aggregated invertebrate data from various sources to assemble data for modeling in two ecoregions in Oregon and one in California. Our goal was to compare the performance of models developed using multiple linear regression (MLR) techniques with models developed using three relatively new techniques: classification and regression trees (CART), random forest (RF), and boosted regression trees (BRT). We used tolerance of taxa based on richness (RICHTOL) and ratio of observed to expected taxa (O/E) as response variables and land use/land cover as explanatory variables. Responses were generally linear; therefore, there was little improvement to the MLR models when compared to models using CART and RF. In general, the four modeling techniques (MLR, CART, RF, and BRT) consistently selected the same primary explanatory variables for each region. However, results from the BRT models showed significant improvement over the MLR models for each region; increases in R² from 0.09 to 0.20. The O/E metric that was derived from models specifically calibrated for Oregon consistently had lower R² values than RICHTOL for the two regions tested. Modeled O/E R² values were between 0.06 and 0.10 lower for each of the four modeling methods applied in the Willamette Valley and were between 0.19 and 0.36 points lower for the Blue Mountains. As a result, BRT models may indeed represent a good alternative to MLR for modeling species distribution relative to environmental variables.     

A System Dynamics Model for Conjunctive Management of Water Resources in the Snake River Basin

The Pacific Northwest is expected to witness changes in temperature and precipitation due to climate change. In this study, we enhance the Snake River Planning Model (SRPM) by modeling the feedback loop between incidental recharge and surface water supply resulting from surface water and groundwater extraction for irrigation and provide a case study involving climate change impacts and management scenarios. The new System Dynamics‐Snake River Planning Model (SD‐SRPM) is calibrated to flow at Box Canyon Springs located along a major outlet of the East Snake Plain Aquifer. A calibration of the model to flow at Box Canyon Springs, based on historic diversions (1950‐1995) resulted in an r² value of 0.74 and a validation (1996‐2005) r² value of 0.60. After adding irrigation entities to the model an r² value of 0.91, 0.88, and 0.87 were maintained for modeled vs. observed (1991‐2005) end‐of‐month reservoir content in Jackson Lake, Palisades, and American Falls, the three largest irrigation reservoirs in the system. The scenarios that compared the impacts of climate change were based on ensemble mean precipitation change scenarios and estimated changes to crop evapotranspiration (ET). Increased ET, despite increased precipitation, generally increased surface water shortages and discharge of springs. This study highlights the need to develop and implement models that integrate the human‐natural system to understand the impacts of climate change.     

Studies of corrosion tendency of drinking water in the distribution system at the University of Benin

Ensemble of corrosion indices was combined to study the corrosion tendency of the drinking water supply at the University of Benin, Nigeria. The experimental results were analysed in terms of three corrosion indices-Langelier Index, Ryznar Index and Larson–Skold Index. According to the evaluation, the Langelier Index ranged from −5.569 to −3.684, Ryznar Index was between 13.340 and 16.418 while the Larson–Skold Index was between 1.191 and 31.750. Results indicated that the water may be corrosive. A regression of these indices on iron concentration (ppm) showed that Langelier Index, Ryznar Index and Larson–Skold Index have R ² of 0.5868, 0.6577 and 0.7063, respectively. The positive correlation between iron levels and the corrosion indices suggested that iron levels were directly related to increase in corrosion tendency.