River Surface Research Articles

Long-term spatiotemporal changes of surface water and its influencing factors in the mainstream of Han River, China

Study regionHan River mainstream, China. Study focusHuman activities and climate change are synergistically impacting on the spatiotemporal features of Han River surface water, which affects the ecological environment evolution and economic development of the basin. However, the long-term changes of the area and spatial characteristics of Han River surface water have not been well quantified for lack of sufficient data and data processing powers. The study produced the annual surface water products of Han River mainstream from 1986 to 2020 based on Google Earth Engine and Landsat images, and then analyzed the long-term spatiotemporal dynamics of surface water and the influence of climatic factor and water conservancy projects construction. New hydrological insights for the regionDuring the past 35 years, the surface water area of Han River mainstream has shown a fluctuating upward trend overall, and the dynamic changes have obvious regional and phased differences. Precipitation is the main climatic factor affecting the surface water area changes. With precipitation changes and the implementation of water conservancy projects in Han River, the surface water area has increased significantly and many reaches have gradually widened in upstream, while the surface water area has slowly decreased and many reaches have gradually narrowed in mid-downstream. The latest information about the spatiotemporal dynamics of Han River surface water provided by the research can be used for the optimal allocation and management of the river’s water resources.

Accumulation and ecotoxicological risk assessment of heavy metals in surface sediments of the Olt River, Romania

Heavy metal pollution of river freshwater environments currently raises significant concerns due to the toxic effects and the fact that heavy metal behavior is not fully understood. This study assessed the contamination level of eight heavy metals and trace elements (Cr, Ni, Cu, Zn, As, Pb, Cd, and Hg) in the surface sediments of 19 sites in 2018 during four periods (March, May, June, and October) in Olt River sediments. Multivariate statistical techniques were used, namely, one-way ANOVA, person product-moment correlation analysis, principal component analysis, hierarchical cluster analysis, and sediment quality indicators such as the contamination factor and pollution load index. The results demonstrated higher contents of Ni, Cu, Zn, As, Pb, Cd, and Hg, with values that were over 2.46, 4.40, 1.15, 8.28, 1.10, 1.53, and 3.71 times more, respectively, compared with the national quality standards for sediments. We observed a positive significant statistical correlation (p < 0.001) in March between elevation and Pb, Ni, Cu, Cr, and Zn and a negative correlation between Pb and elevation (p = 0.08). Intermetal associations were observed only in March, indicating a relationship with river discharge from spring. The PCA sustained mainly anthropogenic sources of heavy metals, which were also identified through correlation and cluster analyses. We noted significant differences between the Cr and Pb population means and variances (p < 0.001) for the data measured in March, May, June, and October. The contamination factor indicated that the pollution level of heavy metals was high and significant for As at 15 of the 19 sites. The pollution load index showed that over 89% of the sites were polluted by metals to various degrees during the four periods investigated. Our results improve the knowledge of anthropogenic versus natural origins of heavy metals in river surface sediments, which is extremely important in assessing environmental and human health risks and beneficial for decision-maker outcomes for national freshwater management plans.

River Slope Observation From Spaceborne GNSS-R Carrier Phase Measurements: A Case Study

This letter demonstrates the potential of estimating river surface slope using space-borne global navigation satellite system-reflectometry (GNSS-R) carrier phase measurements. A case study is presented where the slopes of several segments of the Orinoco river are retrieved based on the processing of raw intermediate-frequency (IF) data recorded by the cyclone global navigation satellite system (CYGNSS) mission. The retrieved river slopes mostly vary between 3.9 and 5.1 cm/km and are in agreement with the surveyed mean slope of 4.5 cm/km for the lower Orinoco River. The correction and calibration of several systematic errors, i.e., CYGNSS satellite orbit errors, tropospheric and ionospheric effects, and river surface reference height error, are discussed in this letter. For the Orinoco River case study, the river slope retrieval is calibrated using the reflection signal over a nearby lake to mitigate the mis-modeled and unmodeled errors. Analysis of the specular point (SP) tracks of GPS reflection signal indicates that CYGNSS has the potential to provide river slope observations for rivers that have sufficient width with high temporal and spatial resolutions.

River Surface Analysis and Characterization Using FMCW Radar

Today’s warning systems for floods or droughts require sensor applications that provide a vast array of information. Current systems provide insight into either the water level or the river velocity. In order to obtain additional parameters for the characterization of the flow behavior in a noncontact manner, a frequency-modulated continuous-wave radar with chirp sequence is used. Since these sensors provide range and velocity information but also enable gathering of additional parameters. The presented measurements in this contribution were performed at four different rivers with a commercially available radar sensor. The results of the classical postprocessed 2-D fast Fourier transform are used as basis for an imagewise processing approach to obtain additional features for classifying the behavior of the river surface. For this purpose, the framewise enveloping velocities are depicted in a time sequence. Due to processing the detected reflection patterns in relation to time, a characteristic pattern for river flow profiles can be extracted. By reducing the information using time averaging, characteristic features for different flows can be extracted from the spatial envelope velocity distribution. In particular, the resulting insights lead to characteristic features for single flow distributions that enable novel monitoring systems with new possibilities to classify rivers using radar sensors.

Distribution and Effects of Microplastics as Carriers of Heavy Metals in River Surface Sediments

Distribution and Effects of Microplastics as Carriers of Heavy Metals in River Surface Sediments

Deep Learning Based River Surface Ice Quantification Using a Distant and Oblique-Viewed Public Camera

Deep Learning Based River Surface Ice Quantification Using a Distant and Oblique-Viewed Public Camera

Distribution of Microplastics and Effects as Carriers of Heavy Metals in River Surface Sediments

Distribution of Microplastics and Effects as Carriers of Heavy Metals in River Surface Sediments

Distribution and Effects of Microplastics as Carriers of Heavy Metals in River Surface Sediments

Distribution and Effects of Microplastics as Carriers of Heavy Metals in River Surface Sediments

Velocity Distribution Inversion Method Based on the RANS Equations Using Microwave Doppler Radar

Microwave Doppler radar has been used to make non-contact river flow measurements and offers a high resolution and a wide range. However, discharge estimation with microwave Doppler radar is challenging due to the inability to measure flow velocity below the river surface based on the Doppler shift. To address this problem, a velocity distribution inversion method based on the Reynolds-Averaged Navier-Stokes (RANS) equations is proposed. A classical two-region model which divides the flow into the inner region and the outer region is applied. Velocity at the inner-outer boundary is estimated using surface velocity and the log law is used to estimate velocity distribution of the inner region. Taking velocities at the surface and the inner-outer boundary as boundary conditions, the velocity distribution in the outer region is derived by solving the RANS equations. Discharge is calculated by combining the proposed method with the velocity-area method. The results illustrated that the velocity distribution obtained by the proposed method and measured by an acoustic instrument are in reasonable agreement. Discharge estimated using the obtained velocity distribution has an error less than 10%. The work indicates the potential of microwave Doppler radar to retrieve the river cross section velocity distribution and estimate discharge.

Hydrological Modeling of Karst Watershed Containing Subterranean River Using a Modified SWAT Model: A Case Study of the Daotian River Basin, Southwest China

Karst watershed refers to the total range of surface and underground recharge areas of rivers (including subterranean rivers and surface rivers) in karst areas. Karst water resources, as the primary source of domestic water supply in southwest China, are vital for the social and economic development of these regions. It is greatly significant to establish a high-precision hydrological model of karst watershed for guiding water resources management in karst areas. Choosing the Daotian river basin in the Wumeng Mountains of Southwest China as the study area, this paper proposed a method for simplifying karst subterranean rivers into surface rivers by modifying the digital elevation model (DEM) based on a field survey and tracer test. This method aims to solve the inconsistency between the topographical drainage divides and actual catchment boundaries in karst areas. The Soil and Water Assessment Tool (SWAT) model was modified by replacing the single-reservoir model in the groundwater module with a three-reservoir model to depict the constraints of multiple media on groundwater discharge in the karst system. The results show that the catchment areas beyond topographic watershed were effectively identified after simplifying subterranean rivers to surface rivers based on the modified DEM data, which ensured the accuracy of the basic model. For the calibration and two validation periods, the Nash–Sutcliffe efficiencies (NSE) of the modified SWAT model were 0.87, 0.83, and 0.85, respectively, and R2 were 0.88, 0.84, and 0.86, respectively. The NSE of the modified SWAT model was 0.09 higher than that of the original SWAT model in simulating baseflow, which effectively improved the simulation accuracy of daily runoff. In addition, the modified SWAT model had a lower uncertainty within the same parameter ranges than the original one. Therefore, the modified SWAT model is more applicable to karst watersheds.

Evaluating Fecal Indicator and Pathogen Relationships in Sewage Impacted Surface Waters to Blend with Reclaimed Water for Potable Reuse in North Carolina.

Surface waters used for drinking water supply often receive upstream wastewater effluent inputs, resulting in de facto wastewater reuse for drinking water and recreation. As populations grow, demands on water supplies increase. As this trend continues, it creates the need to understand the risks associated with such reuse. In North Carolina, potable reuse has been proposed as a combination of at least 80% surface water with up to 20% tertiary-treated, dual-disinfected, reclaimed wastewater, which is then stored for 5 days and further treated using conventional drinking water treatment methods. The state of North Carolina has set standards for both intake surface water and for the reclaimed water produced by wastewater utilities, using indicator microorganisms to measure compliance. The goal of this study was to quantify fecal indicator microorganisms, specifically E. coli, coliphages, and C. perfringens as well as key pathogens, specifically Salmonella spp. bacteria, adenoviruses, noroviruses, and the protozoan parasites Cryptosporidium and Giardia, in two types of water representing potential candidates for potable reuse in North Carolina, (1) run of river surface water and (2) sewage-impacted surface waters, with the purpose of determining if there are predictive relationships between these two microorganism groups that support microbial indicator reliability.

Comparison of software accuracy to estimate the bed grain size distribution from digital images: A test performed along the Rhine River

AbstractThe quantification of the bed grain size distribution (GSD) of river surfaces is primarily conducted through manual approaches in the field. These methods are time consuming and not able to accurately represent the spatial diversity of the grain size distribution of rivers. Recently, several software programs and procedures have been developed using semi‐automatic and automatic methods to estimate bed GSD from digital imagery. The purpose of this study is to compare softwares accuracy between reference GSDs and estimated GSDs using geometric approaches (Basegrain software and a procedure developed on ImageJ), statistical approaches (digital grain size [DGS] and PebbleCounts softwares), and a machine learning framework (SediNet). This study evaluates ten digital images recorded along the Rhine River downstream of the city of Basel. The results showed that all software programs considerably underestimated the manually measured GSDs. Nevertheless, it is possible to significantly improve the estimation of bed GSD by applying calibration laws. Both DGS and Basegrain softwares are reliable to estimate the GSD, while the three others softwares are accurate for percentiles equal and higher than the D50. After linear regression correction, the mean normalized root mean square error of percentile errors did not exceed 13% for DGS and Basegrain software, while the others did not exceed 22% for percentiles coarser than the D50.

A photogrammetry-based variational optimization method for river surface velocity measurement

The ease of access to media resources and computational power has recently generated interest in using vision-based approaches for hydraulic monitoring. A key challenge for non-intrusive, image-based hydrology measurement methods is incorporating different hydraulic variables as prior knowledge with image information. We propose a photogrammetry-based method called L1-Diffusion derived from the convection–diffusion equation commonly used in hydrodynamics with an additional regularization term to estimate the fluid motion field in the image plane, from which the free surface velocity can be further obtained using the photogrammetric projection relationship between the image plane and world coordinates. The inverse problem is used to discuss the relationship between the widely used space–time image velocimetry (STIV) and the proposed L1-Diffusion. To validate the proposed method, unmanned aerial vehicle (UAV) images as well as in-situ acoustic Doppler current profiler (ADCP) experiments were carried out. Based on comparison results with the ADCP measurement and vision-based flow field estimation, the newly proposed L1-Diffusion algorithm can accurately and efficiently estimate the free surface velocity of a river from the image sequences in a variety of scenarios.

Management model Management Model of Unda River in the Development of Traditional Village Based on Custom Village in Paksebali Village Klungkung Bali

This research is intended to identify the water resources management model carried out by Paksebali Village in the development of Tourism Villages in Klungkung Regency. Water resources (SDA) managemet in the development of tourism villages through coexistence between the Customary Villages and the Dinas (state) Villages which are oriented towards empowering rural communities for community welfare. So the Paksebali Village can coexit both the legal structure and the legal substance of the use of Unda River's surface water as a water attraction, restaurant, prewed in the development of new tourist destinations in Klungkung Regency through the development of a tourism village.

A study of saltwater intrusion in the Kallada River, southwest coast of Kerala, India

AbstractMunroe Island in Kollam District of Kerala is a typical backwater village situated at the confluence of the Ashtamudi backwater and the Kallada River system. It is an amalgamation of eight small islands with a total area of 13.4 km2. Salinity intrusion has been a serious threat in the downstream areas of Kallada River for more than one and half decades, affecting the groundwater quality of Munroe Island. The present study focuses on the seasonal variation in physico-chemical characteristics of the underground water system of Munroe Island and Kallada River with special reference to saline water intrusion. Physico-chemical analysis of ground water revealed that samples were unsuitable for drinking due to higher content of Na, Ca, and K. Microbiological analysis of island groundwater showed the presence of coliform and E. coli bacteria above the permissible limit. Results indicated a significant correlation between salinity and major anions viz., Cl− and SO42 and cations viz., Mg, Na, Ca, and K of both river surface and island groundwater. In the present study it is clearly evident the occurrence of saltwater intrusion in Kallada River from Ashtamudy Lake and the its severity become higher during premonsoon season. The results also indicated that the salinity increase in Kallada River has a negative impact on island groundwater quality. The paper also suggests suitable management strategies for overcoming the saltwater intrusion thereby upgrading island sustainability.

Spatiotemporal variability of global river extent and the natural driving factors revealed by decades of Landsat observations, GRACE gravimetry observations, and land surface model simulations

Rivers are among the most dynamic components in Earth's terrestrial water cycle and provide critical ecosystem services. Yet, the spatiotemporal variability of river surface extents remains largely unquantified at the global scale. Satellite remote sensing provides a promising alternative to in-situ observations, which can enable a more comprehensive survey and systematic analysis of global rivers at fine spatial resolutions. The study examines the spatiotemporal variability of river surface extent globally and its natural driving factors, by combining the use of Landsat-based Global Surface Water (GSW) and Global River Widths from Landsat (GRWL) databases. In addition to examining the long-term mean river surface extent in various climate zones, we perform statistical analyses to correlate monthly times series of fractional river extent with the terrestrial water storage (TWS) components obtained from the Gravity Recovery and Climate Experiment (GRACE) satellite observation and the Global Land Data Assimilation System (GLDAS) model simulations. Results show that the spatiotemporal variability of water presence in rivers can be explained well via differentiating climate zones. The analysis also shows that 52.7% of the global maximum river extent is covered by water less than half of time. Changes of fractional river extent are found to be highly correlated with groundwater storage in low- and mid-latitudes, whereas snow melting dominates the river dynamics in high latitudes. By examining the extremes of fractional river extent, we found that the abrupt changes of fractional river extent are well linked to precipitation anomalies in the equatorial, arid, and warm temperate areas. This study offers an innovative perspective to study spatiotemporal dynamics of rivers by combining optical remote sensing (Landsat), gravimetry observations (GRACE), and land surface simulations; and it highlights the significant role of low-flow-generating processes (snow melting, infiltration, and recharge-discharge) in controlling river dynamics in certain regions, which warrants future investigation.

Biodegradation Model of Dissolved Organic Matter during River Bank Filtration, at Al-Qurain City, Sharkyia Governorate, Egypt

Riverbank filtration is carried out at natural conditions and may occur by lowering the groundwater stream under the surface water levels either by hydraulic boundaries such as a bank of channels or by groundwater abstraction at pumping wells. In addition, the mixing of the infiltrated river water with groundwater at the pumping outlet well and the bank filtrate's retention time have been studied and identified as one of the critical parameters that determine the efficiency of riverbank filtration and water quality. RBF water results of pH, conductivity, TDS, hardness, color, alkalinity, nitrate, nitrite, phosphate, silica, sulphate, chloride, and turbidity were complying with The Egyptian Ministry of Health and WHO guidelines. The present study showed that groundwater's iron and manganese data in the study region did not comply with Egyptian regulation (Fe more than 0.3 mg/l and Mn more than 0.4 mg/l). The river surface water quality results did not comply with Egyptian regulation in some parameters, such as microbiological parameters (total bacterial count more than 5000 CFU/ml). The examination of river surface water samples was positive in total coliform fecal coliform examination. The study has shown that the water quality results from the RBF water had high water quality, especially in values of Fe & Mn and also microbiological parameters. The correlation coefficient between DOC recorded, and DOC estimated was very strong (r was 0.94).

Generation and analysis of stage-fall-discharge laws from coupled hydrological-hydraulic river network model integrating sparse multi-satellite data

Current and forthcoming altimetry satellites provide increasing hydraulic visibility of river surface variabilities. However, due to unobservable river bathymetry and friction, as well as the sampling and quality of remote sensing data, discharge estimation remains a challenging problem. This paper proposes a new rigorous methodology to estimate stage-fall-discharge laws at any point of a hydrographic network including backwater affected reaches. This methodology is adapted to operational estimation of discharge from multi-mission satellite altimetry data. The cornerstone of the method is to take advantage of meaningful hydraulic signatures, including water surface slopes, simulated by a river network model optimally combined with multi-satellite noisy observations. This optimal combination of heterogeneous multi-source data with a river network model is achieved through variational data assimilation performed using the DassFlow-HiVDI hydraulic model inflowed by MGB regional hydrological model. It is newly applied to the large and complex Negro-Branco River network in the Amazon basin. An effective model is obtained by variational calibration using Jason-3 and Sentinel-3A and -3B altimetry observations between February 2016 and May 2019. A fairly good fit bellow 1m for 80% of the altimetric observations is obtained. Using this hydrodynamic model as a fine spatio-temporal extrapolator of hydraulic quantities, and in particular of multi-scale water surface slope signatures, the parameters of a general SFD law, as well as the uncertainties estimations, can be constrained at any location within the river network domain using a Bayesian optimization method. This methodology is tested over 4 virtual stations representing various hydraulic complexities. Accurate discharge estimates and meaningful SFD parameters are obtained compared to those of the reference low Froude model at these locations. Analyses are also performed on a reference case with fine-scale bathymetry data of the Garonne River, France. The strength of the method lies in its applicability to noisy multi-satellite data while ensuring physical consistency. It should be applicable in real time to any river basin, using nadir altimetry only or combined to the forthcoming SWOT satellite data. Moreover, the method allows to take full advantage, via the hydrodynamic model, of variable water masks observations and physical segmentations of river networks. The method is implemented in the open source hydrodynamic data assimilation platform DassFlow.

Effect of Physical Characteristics and Hydrodynamic Conditions on Transport and Deposition of Microplastics in Riverine Ecosystem

Microplastic disposal into riverine ecosystems is an emergent ecological hazard that mainly originated from land-based sources. This paper presents a comprehensive review on physical processes involved in microplastics transport in riverine ecosystems. Microplastic transport is governed by physical characteristics (e.g., plastic particle density, shape, and size) and hydrodynamics (e.g., laminar and turbulent flow conditions). High-density microplastics are likely to prevail near riverbeds, whereas low-density particles float over river surfaces. Microplastic transport occurs either due to gravity-driven (vertical transport) or settling (horizontal transport) in river ecosystems. Microplastics are subjected to various natural phenomena such as suspension, deposition, detachment, resuspension, and translocation during transport processes. Limited information is available on settling and rising velocities for various polymeric plastic particles. Therefore, this paper highlights how appropriately empirical transport models explain vertical and horizontal distribution of microplastic in riverine ecosystems. Microplastics interact, and thus feedback loops within the environment govern their fate, particularly as these ecosystems are under increasing biodiversity loss and climate change threat. This review provides outlines for fate and transport of microplastics in riverine ecosystems, which will help scientists, policymakers, and stakeholders in better monitoring and mitigating microplastics pollution.

Impact of Nutrient Load Coming From Göksu River on the Northeastern Mediterranean

Even though North-Eastern Mediterranean (NE Med) is classified as oligotrophic, inshore areas are highly eutrophic due to the discharge of silicate and nitrate-rich surface waters. Aim of this study was to investigate the nutrient load coming from the Göksu River and to estimate its impact on the river domain using satellite images. Monthly, average nitrite (NO2), nitrate (NO3), ammonium (NH4) and phosphate (PO4) load found to be varying between 0.07-31.2 ton/month, 15-1226 ton/month, 0.5-539 ton/month, 1-267 ton/month, respectively. Satellite images showed that surface chlorophyll–a (chl-a) in the river downstream had an increase in both winter and spring seasons as a result of intense precipitation; while, primary production at the offshore regions was mainly impacted by winter mixing and summer stratification. The highest chl-a concentration was observed at the river impacted zone and decreased by more than two folds at the offshore regions. Increased NO3 load observed during winter and spring leads to phytoplankton blooms in the river downstream. The high P content of Göksu River surface waters has increased the productivity at all seasons. As a consequence, correlation analysis showed significant relationship between surface chl-a concentration and PO4-NO3 load.