River Monitoring Stations Research Articles

Evaluation of Energy Complementarity Between Wind, Solar and Water Resources in the Municipality of Lages (Santa Catarina, Brazil)

Purpose: Evaluate the energy complementarity between hydro, wind, and solar resources, and size a wind system and a photovoltaic system to meet the same production of the CGH Caveiras, (Lages/SC). Methods: Wind and solar systems were sized in order to meet the production of CGH Caveiras, from commercial models of equipment and considering data from 2017 to 2019. To assess complementary, data from the same period were obtained from the river monitoring station of ANA and the meteorological station of INMET. Results and Conclusion: In order to meet the demand of CGH Caveiras, only 1 medium sized wind turbine or 915 photovoltaic modules of the chosen models for the study would be necessary, and both systems could be used to compensate for periods of low hydroelectric production. The complementarity was confirmed through Pearson coefficients, where -0.39 was obtained between hydro and wind, and -0.32 between hydro and solar. Temporal complementarity indices, in turn, indicate complementarity between hydro and solar (κhs = 0.778) and between hydro and wind (κhe = 0.611). Research Implications: Both wind and solar resources exhibit complementarity with the hydro patterns of the study area, and either of the proposed systems, wind or photovoltaic solar, could be used in a hybrid manner with the existing hydroelectric system, especially during periods of droughts. Originality/value: Considering the scarcity of energy complementarity studies in Santa Catarina, as well as the impact of climate change on the hydrological regime and hydroelectric power generation, research of this nature is essential for the region’s energy security.

Prediction of Flood Events from the Nekor River Discharge Using the Flood Frequential Analysis Method

This work aims to analyze the yearly most extreme release of the Nekor River monitoring station Tamellaht between 1973 and 2011 and to predict possible future events using the Flood Frequency Analysis Method (FFA). We use the four most estimated distributions that are accessible for prediction of hydrological risk: the three Log Normal, LogPerson Type III, Weibull and GAMMA distributions, and conclude that the Weibull distribution is the suitable statistical model that describe well into our data series, even though the other distributions show data adjustment. Given the Weibull dispersion, the upsides of 580.3 m3/s, 1339 m3/s and 2146.7 m3/s are for the time of return of 10, 50 and 100 years, individually, still high relying upon the semi-dry environment that wins around this region. In fact, the period of extreme returns of the 10th period which can cause dangerous flooding especially considering the mountainous characteristics of the region. The magnitude of the floods is greater because the return period is greater, which explains the semi-arid climate of this region. In addition, a simple statistical description shows that the maximum flow trend has declined over the years, reflecting a possible impact of climate change phenomena.

Mapping Irish Water Bodies: Comparison of Platforms, Indices and Water Body Type

Accurate monitoring of water bodies is essential for the management and regulation of water resources. Traditional methods for measuring water quality are always time-consuming and expensive; furthermore, it can be very difficult capture the full spatiotemporal variations across regions. Many studies have shown the possibility of remote-sensing-based water monitoring work in many areas, especially for water quality monitoring. However, the use of optical remotely sensed imagery depends on several factors, including weather, quality of images and the size of water bodies. Hence, in this study, the feasibility of optical remote sensing for water quality monitoring in the Republic of Ireland was investigated. To assess the value of remote sensing for water quality monitoring, it is critical to know how well water bodies and the existing in situ monitoring stations are mapped. In this study, two satellite platforms (Sentinel-2 MSI and Landsat-8 OLI) and four indices for separating water and land pixel (Normalized Difference Vegetation Index—NDVI; Normalized Difference Water Index—NDWI; Modified Normalized Difference Water Index—MNDWI; and Automated Water Extraction Index—AWEI) have been used to create water masks for two scenarios. In the first scenario (Scenario 1), we included all pixels classified as water, while for the second scenario (Scenario 2) accounts for potential land contamination and only used water pixels that were completed surround by other water pixels. The water masks for the different scenarios and combinations of platforms and indices were then compared with the existing water quality monitoring station and to the shapefile of the river network, lakes and coastal and transitional water bodies. We found that both platforms had potential for water quality monitoring in the Republic of Ireland, with Sentinel-2 outperforming Landsat due to its finer spatial resolution. Overall, Sentinel-2 was able to map ~25% of the existing monitoring station, while Landsat-8 could only map ~21%. These percentages were heavily impacted by the large number of river monitoring stations that were difficult to map with either satellite due to their location on smaller rivers. Our results showed the importance of testing several indices. No index performed the best across the different platforms. AWEInsh (Automated Water Extraction Index—no shadow) and Sentinel-2 outperformed all other combinations and was able to map over 80% of the area of all non-river water bodies across the Republic of Ireland. While MNDWI was the best index for Landsat-8, it was the worst performer for Sentinel-2. This study showed that optical remote sensing has potential for water monitoring in the Republic of Ireland, especially for larger rivers, lakes and transitional and coastal water bodies.

Identifying pollution dynamics using discrete Fourier transform: From an urban-rural river, Central Mexico

The quality of life and human survival is dependent on sustainable development and sanitation of water bodies in an environment. The present research focuses on cyclicity data of more than 750,000 records of parameters associated with the water quality from a rural-urban river monitoring stations in real-time from River Atoyac in Central Mexico. The events detected in the instrumental records correlated with 2528 laboratory and instrumental determinations. The 64 polluting compounds were grouped into inorganic compounds (metals and metalloids) and organic compounds (pesticides, herbicides, hydrocarbons). Metal associated compounds were grouped along mechanical, pharmaceutical and textile industries which associates itself with the entry of polluting components. The cyclicity of the events was detected through Discrete Fourier Transformation time series analysis identifying the predominant events in each station. These highlight the events at 23–26 h corresponding to a circadian pattern of the metabolism of the city. Likewise, pollution signals were detected at 3.3, 5.5, and 12–14 h, associated with discharges from economic activities. Multivariate statistical techniques were used to identify the circadian extremes of a regionalized cycle of polluting compounds in each of the stations.The results of this research allow pollution prevention using a mathematical analysis of time series of different quality parameters collected at monitoring stations in real-time as a tool for predicting polluting events. The DFT analysis makes it possible to prevent polluting events in different bodies of water, allowing to support the development of public policies based on the supervision and control of pollution.

Nutrient and suspended solid concentrations, loads, and yields in rivers across the Lake Winnipeg Basin: A twenty year trend assessment

Study regionThe Lake Winnipeg Basin in the northern Great Plains of North America Study focusAssessment of trends in total nitrogen (TN), total phosphorus (TP) and total suspended solids (TSS) for 18 river stations in the Lake Winnipeg Basin for the period of 1996–2016 using a Weighted Regressions on Time, Discharge and Season (WRTDS) modeling approach. New hydrological insights for the regionWe observed rapidly increasing concentrations, loads, and yields of TN, TP and TSS at most of the evaluated river monitoring stations in the eastern half of the basin. In contrast, nutrient and suspended solid loads tended to exhibit decreasing or stationary trends at most of the more western stations. Trends in nutrients and suspended solids typically corresponded to discharge, particularly in the Red-Assiniboine subdrainage where rapidly increasing nutrient loads were almost exclusively associated with runoff patterns. Our findings will serve as a baseline against which future trend assessments can be compared. Moreover, our findings suggest that land management practices aimed at reducing nutrient loads to Lake Winnipeg should be prioritized towards the rivers of the eastern subdrainages of the Lake Winnipeg basin.

Trends in total nitrogen concentrations in the Three Rivers Headwater Region

It is important to protect the quality of the water in the Three Rivers Headwater Region (TRHR), known as the water tower of China, to guarantee the water security in downstream areas. However, because of a lack of long-term studies that span wide geographical areas, it is difficult to understand how the water resource in the TRHR should be protected. In this paper, we report the findings from our analysis of total nitrogen (TN) concentration data from 39 river monitoring stations for the period from 2012 to 2018. The water quality status was evaluated by comparing the concentrations with the national standards and calculating exceedance ratios for surface water. Trends were calculated with ordinary linear least-squares regression and a weighted least-squares (WLS) meta-analysis method. The results showed that the annual average TN concentrations in the TRHR rivers from 2012 to 2018 ranged from 0.68 to 1.06 mg/L, and were lower than those in the downstream reaches but higher than the global average in natural river waters. For the period from 2012 to 2018, the TN concentrations showed a highly significant increase (0.03 mg/L/year) across the entire TRHR and were increasing and decreasing at 71.8 % and 28.2 % of the stations, respectively. From the trend results, we divided the study area into two zones, one with increasing TN concentrations and one with decreasing TN concentrations. It is found that environmental factors had little influence on TN concentrations in the increasing and decreasing areas, but artificial factors such as population and restoration project areas contributed to the increases in TN concentrations in the increasing area. The TRHR remains a source of clean water in China; however, the water quality should be monitored closely, and measures should be implemented to protect the resource and mitigate the disturbances caused by human activities.

Optimal Location of Water Quality Monitoring Stations Using an Artificial Neural Network Modeling in the Qarah-Chay River Basin, Iran

The economic development, livelihood and drinking water of millions of people in the central plateau of Iran depend on the Qarah-Chay River, but due to a lack of inappropriate monitoring, it has been exposed to destruction and pollution. Consequently, an assessment of the river’s water quality is of utmost importance for both the management of human health and the maintenance of a safe environment, which can be achieved by determining the best locations for pollution monitoring stations along rivers. In this study, artificial neural networks (ANNs) has been used to optimize the locations for Qarah-Chay River monitoring stations in Markazi province, Iran. The data are collected based on the Iranian Water Quality Index (IRWQI), the US National Sanitation Foundation Water Quality Index (NSFWQI) and the Oregon Water Quality Index (OWQI). The database is given to a multilayer perceptron (MLP) neural network along with a geographic information system (GIS). The output of this study identified six pollution monitoring stations on the river, which are mainly downstream due to the accumulation of land uses and the concentration of pollution. The gradient of the MLP network training courses model from the proposed monitoring stations is 0.062299. In addition, the performance evaluation criteria of the proposed MLP model for F1-score, recall, precision and accuracy were 0.85, 0.84, 0.88 and 0.88, respectively. The results obtained help managers to properly monitor the river’s water resources with accuracy, efficiency and lower cost; furthermore, the findings were able to provide scientific references for river water quality monitoring and river ecosystem protection.

A novel assessment considering spatial and temporal variations of water quality to identify pollution sources in urban rivers

It’s vital to explore critical indicators when identifying potential pollution sources of urban rivers. However, the variations of urban river water qualities following temporal and spatial disturbances were highly local-dependent, further complicating the understanding of pollution emission laws. In order to understand the successional trajectory of water qualities of urban rivers and the underlying mechanisms controlling these dynamics at local scale, we collected daily monitoring data for 17 physical and chemical parameters from seven on-line monitoring stations in Nanfeihe River, Anhui, China, during the year 2018. The water quality at tributaries were similar, while that at main river was much different. A seasonal ‘’turning-back” pattern was observed in the water quality, which changed significantly from spring to summer but finally changed back in winter. This result was possibly regulated by seasonally-changed dissolved oxygen and water temperature. Linear mixed models showed that the site 2, with the highest loads of pollution, contributed the highest (β = 0.316, P < 0.001) to the main river City Water Quality Index (CWQI) index, but site 5, the geographically nearest site to main river monitoring station, did not show significant effect. In contrast, site 5 but not site 2 contributed the highest (β = 0.379, P < 0.001) to the main river water quality. Therefore, CWQI index was a better index than water quality to identify potential pollution sources with heavy loads of pollutants, despite temporal and spatial disturbances at local scales. These results highlight the role of aeration in water quality controlling of urban rivers, and emphasized the necessity to select proper index to accurately trace the latent pollution sources.

Impact of Covid-19 Lockdown on Availability of Drinking Water in the Arsenic-Affected Ganges River Basin.

The 2020 COVID-19 pandemic has not only resulted in immense loss of human life, but it also rampaged across the global economy and socio-cultural structure. Worldwide, countries imposed stringent mass quarantine and lockdowns to curb the transmission of the pathogen. While the efficacy of such lockdown is debatable, several reports suggest that the reduced human activities provided an inadvertent benefit by briefly improving air and water quality. India observed a 68-days long, nation-wide, stringent lockdown between 24 March and 31 May 2020. Here, we delineate the impact of the lockdown on groundwater and river sourced drinking water sustainability in the arsenic polluted Ganges river basin of India, which is regarded as one of the largest and most polluted river basins in the world. Using groundwater arsenic measurements from drinking water wells and water quality data from river monitoring stations, we have studied ~700 km stretches of the middle and lower reaches of the As (arsenic)-polluted parts of the river for pre-lockdown (January–March 2020), syn-lockdown (April–May), and post-lockdown periods (June–July). We provide the extent of As pollution-free groundwater vis-à-vis river water and examine alleviation from lockdown as an opportunity for sustainable drinking water sources. The overall decrease of biochemical oxygen demand (BOD) and chemical oxygen demand (COD) concentrations and increase of pH suggests a general improvement in Ganges water quality during the lockdown in contrast to pre-and-post lockdown periods, potentially caused by reduced effluent. We also demonstrate that land use (agricultural/industrial) and land cover (urban-periurban/rural) in the vicinity of the river reaches seems to have a strong influence on river pollutants. The observations provide a cautious optimistic scenario for potentially developing sustainable drinking water sources in the arsenic-affected Ganges river basin in the future by using these observations as the basis of proper scientifically prudent, spatially adaptive strategies, and technological interventions.

Assessment of post-wildfire erosion risk and effects on water quality in south-western Australia

Investigations of wildfire impact on water resources have escalated globally over the last decade owing to an awareness of climate-related vulnerabilities. Within Australia, research into post-wildfire erosion has focused on water supply catchments in the south-eastern region. Here, we examine post-wildfire erosion risk and its potential for water quality impacts in a catchment in south-western Australia. The catchment of the Harvey River, which drains from forested escarpments onto an agricultural coastal plain and into valuable coastal wetlands, was burnt by wildfire in 2016. The aims of this study were to determine erosion risk across contrasting landforms and variable fire severity, using the Revised Universal Soil Loss Equation (RUSLE), and to determine whether post-fire water quality impacts could be detected at permanent river monitoring stations located on the coastal plain. RUSLE outputs showed erosion hot-spots at intersections of steep terrain and high fire severity and that these areas were confined to forested headwaters and coastal dunes. Monthly water quality data showed conspicuous seasonal patterns, but that sampling frequency was temporally too coarse to pick up predicted event-related effects, particularly given that the pre-existing monitoring sites were distal to the predicted zone of contamination.

The deteriorating nutrient status of the Berg River, South Africa

The upper catchment area of the Berg River in the Western Cape, South Africa, supplies most of Cape Town and its suburbs with freshwater, in addition to providing water for irrigation purposes along the middle and lower reaches of the river. This study investigates the nutrient status of the Berg River and long-term trends therein. It is shown that inorganic nitrogen and phosphorus levels increase downstream by a factor of more than 10, in response to anthropogenic inputs. Similarly, nutrient levels fluctuate seasonally by more than an order of magnitude, in response to input from diffuse and point sources of pollution. These changes of more than 1 000% far exceed the 15% maximum change stipulated by the South African water quality guidelines for aquatic ecosystems. Total phosphorus levels indicate that hypertrophic conditions prevail at least episodically at all of the Berg River monitoring stations and most of the time at some of them. Additionally, river water phosphate levels show a dramatic increase over the past 20 years. There is also strong evidence that the trophic status of the Berg River is very sensitive to reduced river runoff. The implication is that the construction of the new Berg River Dam in the upper catchment area of the Berg River will exacerbate the existing situation, threatening ecosystem services, human health and lucrative agricultural activities.Keywords: Berg River, eutrophication, nutrients, nitrate, phosphate

Impact of urban pressure on the spatial and temporal dynamics of PAH fluxes in an urban tributary of the Seine River (France)

Polycyclic aromatic hydrocarbons (PAHs) produced by numerous anthropogenic activities are ubiquitous in the environment and have become a priority concern due to their potential severe biological impacts. A better understanding of PAH transfer at the catchment scale is therefore necessary to improve the management of PAH contaminants and protect rivers. Furthermore, the impact of changes in hydrological regimes and land uses on PAH fluxes should be specifically investigated. Accordingly, the current research monitors the contamination in atmospheric fallout, soils and rivers in a 950-km2 catchment (Orge River) characterized by an increasing urban gradient in downstream direction. During an entire hydrological year, river water contamination was quantified through regular sampling of both particulate and dissolved material at four river-monitoring stations, reflecting the increasing urbanization gradient. The significant input of PAHs from urban areas in downstream river sections corresponded to a specific PAH flux that reached 23 g km−2 y−1 despite the low sediment yield. Moreover, the comparison with runoff-specific fluxes reported in the literature underlined the major impact of urban runoff on the Orge River water and sediment quality. Nevertheless, the annual PAH load exported by the river (21 kg y−1) remained lower than the PAH inputs from atmospheric fallout (173 kg y−1), demonstrating the continuous accumulation of PAH from atmospheric fallout in the catchment soils. Consequently, the notably large PAH stock (close to 1000 tons) resulting from historical contamination of this early-industrialized region continues to increase due to ongoing atmospheric inputs.

Long-Term Trends in the Concentrations of Legacy Persistent Organochlorine Pollutants in Whole-Body Samples of Bottomfeeding Fish from Six Large River Monitoring Stations in Kansas

Although persistent organic pollutants (POPs) in bottom-feeding fish have been monitored for decades in Kansas, no information regarding long term trends has been published. Therefore, in this study we examined fish tissue data collected from six long-term, large-river, fish tissue monitoring stations in Kansas for trends in POP concentrations. Based on the findings we also forecasted future persistence and discussed the practical implications of our findings. The data we examined consisted of whole-body composited multi-fish samples collected over periods spanning 21–33 years. Common carp was the target species, but occasionally other bottom-feeding species were substituted when necessary. The sample constituents examined were dieldrin, technical chlordane, dichlorodiphenyltrichloroethanes (DDTs), and polychlorinated biphenyls (PCBs). Constituent data were normalized by lipid content prior to analysis, however, because lipid content was not measured during certain time periods, multiple imputation (MI) and analyses techniques (SAS 2014) were employed to overcome the data gaps. The lipidnormalized data were analyzed for time trends using robust non-parametric techniques, which were capable of handling both censored and uncensored data. Because of expected high sample variability, correlations between time and constituent concentrations were considered statistically probable (significant) at p-values < 0.1. Where correlations were significant, the trend line equations were extrapolated to current method reporting limit (RLs) concentrations and 90% prediction intervals (PIs) were constructed around the forecasted RL/year intercepts. Fifteen of the 24 analyses resulted in statistical significance, but no monitoring station experienced significantly declining trends among all four constituents, nor did any constituent significantly decrease across all six stations. The average forecasted RL/year intercept was 2050 (PI = /- 23 years), however some upper PIs suggested some POPS could remain detectable in fish for at least another century.

Estimating Hourly Water Temperatures in Rivers Using Modified Sine and Sinusoidal Wave Functions

AbstractAir temperature is often used to estimate stream water temperature by developing regression models. A method using modified sine functions and sinusoidal wave functions (MSSWF) was proposed for estimating hourly water temperatures in rivers. Estimates of daily maximum and minimum water temperatures from sine functions (SFs) were corrected using linear regressions as functions of deviations of estimates of daily maximum and minimum air temperatures from SFs, and then modified sinusoidal wave functions (SWFs) were used to estimate hourly water temperatures. Excellent agreement was found between observed and estimated hourly water temperatures using MSSWF models developed for 13 rivers in Alabama. The average Nash-Sutcliffe efficiency (NSE) for the MSSWF models developed for individual rivers is 0.94. The distance of a river monitoring station from the weather station has very little effect on the performance of individual MSSWF models when the distance is less than approximately 300 km. A lumped MSS...

A framework for coupling explanation and prediction in hydroecological modelling

Causal explanation and empirical prediction are usually addressed separately when modelling ecological systems. This potentially leads to erroneous conflation of model explanatory and predictive power, to predictive models that lack ecological interpretability, or to limited feedback between predictive modelling and theory development. These are fundamental challenges to appropriate statistical and scientific use of ecological models. To help address such challenges, we propose a novel, integrated modelling framework which couples explanatory modelling for causal understanding and input variable selection with a machine learning approach for empirical prediction. Exemplar datasets from the field of freshwater ecology are used to develop and evaluate the framework, based on 267 stream and river monitoring stations across England, UK. These data describe spatial patterns in benthic macroinvertebrate community indices that are hypothesised to be driven by meso-scale physical and chemical habitat conditions. Whilst explanatory models developed using structural equation modelling performed strongly (r2 for two macroinvertebrate indices = 0.64–0.70), predictive models based on extremely randomised trees demonstrated moderate performance (r2 for the same indices = 0.50–0.61). However, through coupling explanatory and predictive components, our proposed framework yields ecologically-interpretable predictive models which also maintain the parsimony and accuracy of models based on solely predictive approaches. This significantly enhances the opportunity for feedback among causal theory, empirical data and prediction within environmental modelling.

Controls Over Base Cation Concentrations in Stream and River Waters: A Long-Term Analysis on the Role of Deposition and Climate

Significant concern has emerged over the past decades regarding decreases in available base cations (that is, calcium, magnesium, potassium, and sodium) in forest soils and surface waters. Base cations (BCs) are important for buffering against changes in soil and water acidity, and their concentrations can be indicative of environmental management problems such as those linked to acid deposition and land use. Climate variability is also a potentially large factor influencing the dynamics of BCs in soils and surface waters, but our understanding of these interactions at broad scales remains elusive. We used a hierarchical Bayesian model to evaluate the long-term (1990–2010) patterns and drivers of BC concentrations for 60 stream and river monitoring stations across Sweden. Results indicated that the long-term trends in concentration, and the associated environmental drivers, differed among individual BCs and geographical regions. For example, we found that concentrations of Ca2+, Mg2+, K+, and Na+ have decreased in southern Sweden since 1990 and that this is strongly related to concurrent declines in sulfate (SO4 2−) over the same period of record. In contrast, concentrations of Ca2+, Mg2+, K+, and Na+ in northern Sweden did not exhibit significant directional trends, despite declines in SO4 2−, nitrate (NO3 −), and chloride (Cl−) over the same period. Instead, BC dynamics in the north are characterized by inter-annual variability that is most closely linked to climate variables. Results suggest that the interaction between climatic variability and historical acid deposition determines the regional pattern and long-term trends of BC concentrations across streams and rivers of Sweden. Understanding the strength of the interaction between climate features and historic deposition will greatly improve our ability to predict long-term trends of Ca2+, Mg2+, K+, and Na+ and their inter-annual dynamics in the future.

Regression-based Daugava River Flood Forecasting and Monitoring

The paper discusses the application of linear and symbolic regression to forecast and monitor river floods. Main tasks of the research are to find an analytical model of river flow and to forecast it. The challenges are a small set of flow measurements and a small number of input factors. Genetic programming is used in the task of symbolic regression. To train the model, historical data of the Daugava River monitoring station near Daugavpils city are used. Several regression scenarios are discussed and compared. Models obtained by the methods discussed in the research show good results and applicability in predicting the river flow and forecasting of the floods.

Trend of herbicide loads in the river Rhine and its tributaries

Many policies have been established and actions have been taken to reduce pesticide pollution of surface waters. However, the effectiveness of these initiatives was rarely tested on an empirical basis. This study suggests that evidence on the policy effects can be evaluated by analyzing the temporal changes in the loads rather than the concentrations of active ingredients in rivers. It is shown that the long-term change of pesticide emissions into surface waters can be tested statistically by the number of upward versus downward trends in river load. To evaluate the situation in Germany, 57 concentration time series of 14 herbicide substances at 7 river monitoring stations with a minimum of 24 analyses per year were assembled and annual substance river loads were calculated. The longest time course was 17 years (1990-2006). The significance of trends of data rows was analyzed by an univariate Mann-Kendall test that evaluates 27 (47.4%) of the 57 time series as statistically significant downward trended. It took a period of 10 years and longer before the high annual atrazine and simazine loads measured in the years 1990-1991 had been diminished to a drastically lowered level after ban of the herbicides. Data are available on the yearly consumption of 8 substances used in German agriculture. A total of 36 time series for this subset were tested with a partial Mann-Kendall test with the consumption as covariance factor, which reduces the number of significant trends in river load noticeably. Based on this test, only 7 (19.4% of 36) declining time series remain. As a result, the intended effect of measures to reduce surface water contamination by the use of pesticides seems to be only partially successful, however, the database to justify this statement is small. For the water monitoring strategies in Germany, it is recommend to enhance the sampling frequency at river stations. A minimum of a semimonthly sampling interval would facilitate the calculation of valuable annual river loads and would therefore allow a pronounced validation of the long-term change of pesticide emission into rivers.

Spatial variations in chemical weathering and CO 2 consumption in Nepalese High Himalayan catchments during the monsoon season

The major ion chemistry of the Marsyandi basin and six of its tributaries in the Nepalese Himalaya have been investigated during the monsoon months of 2002. Weekly water samples taken at 10 river monitoring stations in the Annapurna watershed over the course of 4 months provide chemical weathering data for the region at an unprecedented temporal and spatial resolution. The river chemistry of all but one basin is heavily dominated by carbonate weathering which, compared to silicate weathering, contributes 80 to 97% of the total solute load. This prevalence is due to a combination of (a) intrinsically faster dissolution kinetics of carbonates, (b) relatively high runoff and (c) glacial meltwater and low temperatures at high altitudes resulting in enhanced carbonate solubilities. Monitoring stations with headwaters in the Tethyan Sedimentary Series (TSS) are particularly carbonate-rich and slightly supersaturated with respect to calcite through half of the monsoon season. Silicate weathering in the TSS is driven largely by sulfuric acid and therefore does not contribute significantly to the drawdown of atmospheric CO 2. With respect to the tributaries in the Greater Himalayan Sequence (GHS), carbonate weathering is practically as predominant as for the TSS, in spite of the largely felsic lithology of the GHS. Relative to the TSS, the primary proton source in the GHS has shifted, with at least 80% of the protons derived from carbonic acid. Averaged over the whole field area, the CO 2 fluxes, based on silicate-derived Ca and Mg, are considerably lower than the global average. Assuming that this study area is representative of the entire range, we conclude that in situ weathering of the High Himalayas does not represent a significant sink of atmospheric carbon dioxide, despite the presence of a watershed south of the GHS that is characterized by a four times higher CO 2 consumption rate than the global average. Silicate weathering rates of all basins appear to be climate controlled, displaying a tight correlation with runoff and temperature. Given the extremely low chemical weathering under transport-limited conditions in high-altitude crystalline terrains outside of the monsoon season, this would result in virtually no chemical exhumation for 2/3 of the year in such a cold and arid climate, north of the rain shadow cast by the High Himalayas.

Continuous, diurnally fluctuating turbidity currents in Kluane Lake, Yukon Territory

Modern sedimentary processes were studied in Kluane Lake, Yukon Territory, to determine the spatial and temporal patterns of sediment distribution in a large, dynamic, glacier-fed lake. Data from a river monitoring station, moored instruments, sediment traps, and water-column profiles in the lake were used to document lacustrine sedimentary processes in 2006 and 2007. During the peak melt season, the suspended sediment concentration of Slims River is weakly dependent on river discharge and can reach up to 5 g L–1, although a diurnal range between 1 and 2 g L–1 is more commonly observed. The high suspended sediment load in the river generates continuous, diurnally fluctuating turbidity currents in Kluane Lake with maximum velocities up to 0.6 m s–1. During times of peak flow, variations in velocity can be traced to beyond 4 km from the river mouth. The vertical concentration profiles, mass accumulation rates, and suspended sediment loads show distinctive longitudinal variations; the highest rate of accumulation occurs ∼1 km from the point of inflow, which is concurrent with a distinct change in flow structure. Diurnal laminations are apparent in sediment traps close to the point of inflow and can be directly linked to variations in current velocity; however, these laminations do not occur consistently over space or time. These results suggest that long-term measurement records of lacustrine turbidity currents provide valuable insights regarding the multiple scales of environmental variability and have important implications for paleoenvironmental reconstruction using lacustrine sediments.