scholarly journals Performance of selected macroinvertebrate-based biotic indices for rivers draining the Merendon Mountains region of Honduras

2013 ◽  
Vol 5 (1) ◽  
pp. 45-54 ◽  
Author(s):  
Paul O’Callaghan ◽  
Mary Kelly-Quinn
Keyword(s):  
Water Quality ◽  
Stream Water ◽  
Cloud Forest ◽  
Water Quality Monitoring ◽  
The United States ◽  
Agricultural Activity ◽  
Macroinvertebrate Communities ◽  
Stream Water Quality ◽  
Forest Park ◽  
Forest Parks

Cusuco National Park, a cloud forest park situated in the Merendon Mountains region of Honduras, is home to considerable biodiversity with a very high degree of endemism. Like many other cloud forest parks in Central America, it was declared a protected area not because of its biodiversity, but mainly because of its function in protecting river water quality in the headwaters draining the park. Illegal and authorised forest clearance for agricultural activity such as coffee production and pasture for animals and diffuse inputs from settlements are expected to affect aquatic macroinvertebrate communities and the water quality of the rivers in the park. There is however very limited water quality monitoring, mainly because biological indices such as those in use in Europe and the United States among others have not been developed in Honduras. A research project based on macroinvertebrates was initiated in 2009 to develop a water quality index for the area. Sampling was undertaken between June and August 2009 and set out to sample minimally disturbed upper catchments of reference rivers. This paper evaluates the performance of several  indices. A version of the ASPT based on the BMWP calibrated for Costa Rica (BMWP(CR)) performed best.     KEY WORDSBiomonitoring, Honduras, macroinvertebrates, neotropics, upland stream, water quality

scholarly journals The impact of conifer harvesting on stream water quality: the Afon Hafren, mid-Wales

2004 ◽  
Vol 8 (3) ◽  
pp. 503-520 ◽  
Author(s):  
C. Neal ◽  
B. Reynolds ◽  
M. Neal ◽  
H. Wickham ◽  
L. Hill ◽  
...  
Keyword(s):  
Water Quality ◽  
Stream Water ◽  
Strong Support ◽  
Shallow Groundwater ◽  
Water Quality Monitoring ◽  
Quality Data ◽  
Stream Water Quality ◽  
Catchment Scale ◽  
Water Quality Data ◽  
The Impact

Abstract. Results for long term water quality monitoring are described for the headwaters of the principal headwater stream of the River Severn, the Afon Hafren. The results are linked to within-catchment information to describe the influence of conifer harvesting on stream and shallow groundwater quality. A 19-year record of water quality data for the Hafren (a partially spruce forested catchment with podzolic soil) shows the classic patterns of hydrochemical change in relation to concentration and flow responses for upland forested systems. Progressive felling of almost two-thirds of the forest over the period of study resulted in little impact from harvesting and replanting in relation to stream water quality. However, at the local scale, a six years’ study of felling indicated significant release of nitrate into both surface and groundwater; this persisted for two or three years before declining. The study has shown two important features. Firstly, phased felling has led to minimal impacts on stream water. This contrasts with the results of an experimental clear fell for the adjacent catchment of the Afon Hore where a distinct water quality deterioration was observed for a few years. Secondly, there are localised zones with varying hydrology that link to groundwater sources with fracture flow properties. This variability makes extrapolation to the catchment scale difficult without very extensive monitoring. The implications of these findings are discussed in relation to strong support for the use of phased felling-based management of catchments and the complexities of within catchment processes. Keywords: deforestation, water quality, acidification, pH, nitrate, alkalinity, ANC, aluminium, dissolved organic carbon, Plynlimon, forest, spruce, Afon Hafren, podzol

scholarly journals Assessment of Miscanthus Yield Potential from Strip-Mined Lands (SML) and Its Impacts on Stream Water Quality

2019 ◽  
Author(s):  
Kamalakanta Sahoo ◽  
Sudhagar Mani ◽  
Adam M. Milewski ◽  
Nahal Hoghooghi ◽  
Sudhanshu Sekhar Panda
Keyword(s):  
United States ◽  
Water Quality ◽  
Environmental Impacts ◽  
Stream Water ◽  
The United States ◽  
Energy Crops ◽  
Yield Potential ◽  
Land Resource ◽  
Stream Water Quality ◽  
Fertilizer Application Rate

Strip-mined land (SML) disturbed by coal-mining is the non-crop land resource that can be utilized to cultivate high-yielding energy crops such as miscanthus for bioenergy applications. However, the biomass yield potential, annual availability and environmental impacts on growing energy crops in SML are less understood. In this study, we estimated the yield potential of miscanthus (Miscanthus sinensis) in SML and its environmental impacts on local streams using the Soil and Water Assessment Tool (SWAT). After calibration and validation of the SWAT model, the results demonstrated that miscanthus yield potentials were 2.6 (0.8−5.53), 10.0 (1.3−16.0) and 16.0 (1.34−26.0) Mg ha-1 with the fertilizer application rate of 0, 100, and 200 kg-N ha-1 respectively. Furthermore, cultivation of miscanthus in the SML has the potential to reduce sediment (~20%) and nitrate (2.5%−10.0 %) loads reaching to water streams with a marginal increase in phosphorus load. The available SML in the United States could produce about 10 to 16 dry Tg of biomass per year without negatively impacting the water quality. In conclusion, SML can provide a unique opportunity to produce biomass for bioenergy applications, while improving stream water quality in highly dense mining area (the Appalachian region) in the United States.

Real-time water quality monitoring and regression analysis to estimate nutrient and bacteria concentrations in Kansas streams

2002 ◽  
Vol 45 (9) ◽  
pp. 205-219 ◽  
Author(s):  
V.G. Christensen ◽  
P.P. Rasmussen ◽  
A.C. Ziegler
Keyword(s):  
Water Quality ◽  
Real Time ◽  
Stream Water ◽  
Treatment Strategies ◽  
Water Quality Monitoring ◽  
Specific Conductance ◽  
Regression Equations ◽  
Stream Water Quality ◽  
Aquatic Life ◽  
Maximum Daily Loads

An innovative approach currently is underway in Kansas to estimate and monitor constituent concentrations in streams. Continuous in-stream water-quality monitors are installed at selected U.S. Geological Survey stream-gaging stations to provide real-time measurement of specific conductance, pH, water temperature, dissolved oxygen, turbidity, and total chlorophyll. In addition, periodic water samples are collected manually and analyzed for nutrients, bacteria, and other constituents of concern. Regression equations then are developed from measurements made by the water-quality monitors and analytical results of manually collected samples. These regression equations are used to estimate nutrient, bacteria, and other constituent concentrations. Concentrations then are available to calculate loads and yields to further assess water quality in watersheds. The continuous and real-time nature of the data may be important when considering recreational use of a water body; developing and monitoring total maximum daily loads; adjusting water-treatment strategies; and determining high constituent concentrations in time to prevent adverse effects on fish or other aquatic life.

scholarly journals A bayesian hierarchical model to predict spatio-temporal variability in river water quality at 102 catchments

2020 ◽  
Author(s):  
Danlu Guo ◽  
Anna Lintern ◽  
Angus Webb ◽  
Dongryeol Ryu ◽  
Ulrike Bende-Michl ◽  
...  
Keyword(s):  
Water Quality ◽  
Statistical Model ◽  
Temporal Variability ◽  
Stream Water ◽  
Spatial Scales ◽  
Water Quality Monitoring ◽  
Catchment Management ◽  
Stream Water Quality ◽  
Bayesian Hierarchical ◽  
Spatio Temporal

<div> <div> <div> <div>Our current capacity to model stream water quality is limited particularly at large spatial scales across multiple catchments. To address this, we developed a Bayesian hierarchical statistical model to simulate the spatio-temporal variability in stream water quality across the state of Victoria, Australia. The model was developed using monthly water quality monitoring data over 21 years, across 102 catchments, which span over 130,000 km<sup>2</sup>. The modelling focused on six key water quality constituents: total suspended solids (TSS), total phosphorus (TP), filterable reactive phosphorus (FRP), total Kjeldahl nitrogen (TKN), nitrate-nitrite (NO<sub>x</sub>), and electrical conductivity (EC). The model structure was informed by knowledge of the key factors driving water quality variation, which had been identified in two preceding studies using the same dataset. Apart from FRP, which is hardly explainable (19.9%), the model explains 38.2% (NO<sub>x</sub>) to 88.6% (EC) of total spatio-temporal variability in water quality. Across constituents, the model generally captures over half of the observed spatial variability; temporal variability remains largely unexplained across all catchments, while long-term trends are well captured. The model is best used to predict proportional changes in water quality in a Box-Cox transformed scale, but can have substantial bias if used to predict absolute values for high concentrations. This model can assist catchment management by (1) identifying hot-spots and hot moments for waterway pollution; (2) predicting effects of catchment changes on water quality e.g. urbanization or forestation; and (3) identifying and explaining major water quality trends and changes. Further model improvements should focus on: (1) alternative statistical model structures to improve fitting for truncated data, for constituents where a large amount of data below the detection-limit; and (2) better representation of non-conservative constituents (e.g. FRP) by accounting for important biogeochemical processes.</div> </div> </div> </div>

scholarly journals A Bayesian approach to understanding the key factors influencing temporal variability in stream water quality: a case study in the Great Barrier Reef catchments

2021 ◽  
Author(s):  
Shuci Liu ◽  
Dongryeol Ryu ◽  
J. Anugs Webb ◽  
Anna Lintern ◽  
Danlu Guo ◽  
...  
Keyword(s):  
Water Quality ◽  
Great Barrier Reef ◽  
Temporal Variation ◽  
Temporal Variability ◽  
Stream Water ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Barrier Reef ◽  
Key Factors ◽  
Stream Water Quality

Abstract. Stream water quality is highly variable both across space and time. Water quality monitoring programs have collected a large amount of data that provide a good basis to investigate the key drivers of spatial and temporal variability. Event-based water quality monitoring data in the Great Barrier Reef catchments in northern Australia provides an opportunity to further our understanding of water quality dynamics in sub-tropical and tropical regions. This study investigated nine water quality constituents, including sediments, nutrients and salinity, with the aim of: 1) identifying the influential environmental drivers of temporal variation in flow event concentrations; and 2) developing a modelling framework to predict the temporal variation in water quality at multiple sites simultaneously. This study used a hierarchical Bayesian model averaging framework to explore the relationship between event concentration and catchment-scale environmental variables (e.g., runoff, rainfall and groundcover conditions). Key factors affecting the temporal changes in water quality varied among constituent concentrations, as well as between catchments. Catchment rainfall and runoff affected in-stream particulate constituents, while catchment wetness and vegetation cover had more impact on dissolved nutrient concentration and salinity. In addition, in large dry catchments, antecedent catchment soil moisture and vegetation had a large influence on dissolved nutrients, which highlights the important effect of catchment hydrological connectivity on pollutant mobilisation and delivery.

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