Current research on riverine macrolitter does not yet provide a theoretic framework on the dynamics behind its accumulation and distribution along riverbanks. In an attempt to better understand these dynamics a detailed field survey of three months was conducted in which location of macrolitter items within a single groyne field along the Waal riverbanks was tracked. The data provided insight into the daily changing patterns of spatial item distribution with respect to the waterline. Furthermore, the rates of item uptake and deposition were monitored and related to hydrologic fluctuations. Uptake was initiated by rising water levels and was generally higher when the water level increased faster. Deposition occurred continuously, despite hydrologic fluctuations. This caused the riverbank macrolitter budget to be positive during stable or dropping water levels and negative during rising water levels. Although the results show clear patterns an extended monitoring duration is required to fully understand the fate of plastic objects.

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Plastics originating from land are mainly transported to the oceans by rivers. The total plastic transport from land to seas remains uncertain because of difficulties in measuring and the lack of standard observation techniques. A large focus in observations is on plastics floating on the water surface. However, an increasing number of observations suggest that large quantities of plastics are transported in suspension, below the water surface. Available underwater plastic monitoring methods use nets or fish traps that need to be deployed below the surface and are labor-intensive. In this research, we explore the use of echo sounding as an innovative low-cost method to quantify and identify suspended macroplastics. Experiments under controlled and natural conditions using a low-cost off-the-shelf echo sounding device show that plastic items can be detected and identified up to 7 m below the river surface. Eight different debris items (metal can, cup, bottles, food wrappers, food container) were characterized based on their reflection signature. Reflectance from plastic items diverged significantly from organic material and non-plastic anthropogenic debris. During a multi-day trial field expedition in the Guadalete river, Spain, we found that between 0.8 and 6.3 m depth considerable quantities of plastics are transported. As most plastic monitoring and removal strategies focus on the upper layer below the surface (up to approximately 1.5 m depth), a substantial share of the total plastic transport may be neglected. With this paper we 1) demonstrate that echo sounding is a promising tool for underwater plastic monitoring, and 2) emphasize the importance of an improved understanding of the existing plastic loads below the surface.

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Rapid advancements in technologies open up possibilities for water resource authorities to increase their ability to accurately, safely and efficiently establish river flow observation through remote and non-intrusive observation methods. Low-cost Unmanned Aerial Vehicles (UAVS) in combination with Global Navigation Satellite Systems (GNSS) can be used to collect geometrical information of the riverbed and floodplain. Such information, in combination with hydraulic modelling tools, can be used to establish physically based relationships between river flows and permanent proxy. This study proposes a framework for monitoring volatile, dangerous and difficult to access rivers using only affordable and easy to maintain new technologies. The framework consists of four main components: i) establishment of geometry using airborne photogrammetry and bathymetry; ii) physically based rating curve development through hydraulic modelling of surveyed river sections; iii) determination of non-intrusive observations with for instance simple cameras or satellite observations; and iv) evaluating the institutional and societal impacts of using new technology. To establish this framework, a number of research questions require addressing. First, the factors impacting on accuracy of geometrical information of the floodplain terrain and bathymetry need to be investigated. Second the accuracy of a physically based rating curve compared to a traditional rating curve needs to be established. Third, for rapidly changing river segments, it should be investigated if the collection of occasional snapshots of multiple proxies for flow can be used to assess the uncertainty of river flows. The study finally explores the social and institutional impact of using new technologies for remote river monitoring. If these research gaps are addressed, this may strengthen water manager's ability to observe flows and extend observation networks.@en

Rapid improvements in the precision and spatial resolution of Distributed Temperature Sensing (DTS) technology now allows its use in hydrological and atmospheric sciences. Introduced by Euser [Hydrol. Earth Syst. Sci., 18, 2021–2032 (2014)] is the use of DTS for measuring the Bowen ratio (BR-DTS), to estimate the sensible and latent heat flux. The Bowen ratio is derived from DTS measured vertical profiles of the air temperature and wet-bulb temperature. However, in previous research the measured temperatures were not validated, and the cables were not shielded from solar radiation. Additionally, the BR-DTS method has not been tested above a forest before, where temperature gradients are small and energy storage in the air column becomes important. In this paper the accuracy of the wet-bulb and air temperature measurements of the DTS are verified, and the resulting Bowen ratio and heat fluxes are compared to eddy covariance data. The performance of BR-DTS was tested on a 46 m high tower in a mixed forest in the centre of the Netherlands in August 2016. The average tree height is 26 to 30 m, and the temperatures are measured below, in, and above the canopy. Using the vertical temperature profiles the storage of latent and sensible heat in the air column was calculated. We found a significant effect of solar radiation on the temperature measurements, leading to a deviation of up to 3 K. By installing screens, the error caused by sunlight is reduced to under 1 K. Wind speed seems to have a minimal effect on the measured wet-bulb temperature, both below and above the canopy. After a simple quality control, the Bowen ratio measured by DTS correlates well with eddy covariance (EC) estimates (r2 = 0.59). The average energy balance closure between BR-DTS and EC is good, with a mean underestimation of 3.4 W m−2 by the BR-DTS method. However, during daytime the BR-DTS method overestimates the available energy, and during night-time the BR-DTS method estimates the available energy to be more negative. This difference could be related to the biomass heat storage, which is neglected in this study. The BR-DTS method overestimates the latent heat flux on average by 18.7 W m−2, with RMSE = 90 W m−2. The sensible heat flux is underestimated on average by 10.6 W m−2, with RMSE = 76 W m−2. Estimates of the BR-DTS can be improved once the uncertainties in the energy balance are reduced. However, applying e.g. Monin-Obukhov similarity theory could provide independent estimates for the sensible heat flux. This would make the determination of the highly uncertain and difficult to determine net available energy redundant.@en

Over the past ten years, Distributed Temperature Sensing (DTS) has been applied for monitoring many different environmental processes, from groundwater movement, to seepage into streams and canals, to soil moisture, and internal waves in lakes. DTS uses optical fibres, along which temperatures are determined by measuring Raman shifts in light that scatters back after a laser pulse has been sent into the fiber. Over the past decade, performance of DTS equipment has dramatically improved. It is now possible to determine fiber temperatures with 0.05 K accuracy, for each 25 cm along a fiber optic cable. With typical spatial resolutions of 1 m, cable lengths can run up to 5 km. Accuracy improves with integration over longer sampling intervals, but measurements over 60 s can give 0.1 K accuracy with proper in-field calibration. DTS can also be used for atmospheric properties such as air temperature, vapor pressure, and wind speed. This presentation provides a complete overview of recent advances in atmospheric DTS observations. Air temperature is the simplest, as one simply has to suspend a fiber optic cable along the profile of interest. This can be from a balloon or along poles. Care has to be taken to correct for radiative heating of the cable. Using a thin white cable minimalizes radiative effects and normally brings the measured temperature to within 1 K of actual air temperature, sufficient for studies on effects of shading in natural and urban landscapes. It is also possible to correct for radiative heating by modeling in some detail the cable’s thermal behavior or by using two cables of different diameters. Supporting structures may also have an effect on cable temperatures, which should be minimized or corrected for. Water vapor can be measured by comparing the temperatures of wet and dry cables. These wet and dry bulb temperatures allow derivation of humidity profiles, which, in turn, allows for Bowen-ratio type of calculations of latent and sensible heat fluxes. This has proven especially useful in otherwise difficult to measure profiles such as through forest canopies. Wind speed can be measured by including a conductive element in the fiber optic cable and heating the cable actively by sending a current through that element. In effect, the cable then acts as a hot wire anemometer but then over long lengths of cable and with high spatial resolutions. When carefully executed, experiments with heated cables give very detailed insight into turbulent processes in the lower boundary. It is even possible to resolve bigger individual turbulent and sub-meso-scale eddies for studying fast evolving fluid flows (orders of seconds). A comprehensive overview of atmospheric applications will be presented, together with pitfalls, common errors, and practical tips to avoid those in the field. @en

The session that this poster is in, the: “Self-made sensors and unintended use of measurement equipment”, also known as the “MacGyver-session” has had 7 years of scientists contributing their self made devices, hacks and solutions with the hydrological community. In 2009, the first session was held at the AGU fall meeting and since 2011 a session is also organised at the EGU General Assembly. On this poster, and in the accompanying review paper, we will present an overview of the work presented in the last 7 years, cataloging the work of the inventive scientists who have contributed to these successful, and above all: fun, sessions.@en

ype HOBO Weather Station. Measures: rain, wind, gust speed, wind direction, temperature, relative humidity, solar radiation.@en

Already in the 19th century, d’Auria described a discharge measurement technique that applies floats to find the depth-integrated velocity (d’Auria, 1882). The basis of this technique was that the horizontal distance that the float travels on its way to the surface is the image of the integrated velocity profile over depth. Viol and Semenov (1964) improved this method by using air bubbles as floats, but still distances were measured manually until Sargent (1981) introduced a technique that could derive the distances from two photographs simultaneously taken from each side of the river bank. Recently, modern image processing techniques proved to further improve the applicability of the method (Hilgersom and Luxemburg, 2012). In the 2012 article, controlling and determining the rising velocity of an air bubble still appeared a major challenge for the application of this method. Ever since, laboratory experiments with different nozzle and tube sizes lead to advances in our self-made equipment enabling us to produce individual air bubbles with a more constant rising velocity. Also, we introduced an underwater camera to on-site determine the rising velocity, which is dependent on the water temperature and contamination, and therefore is site-specific. Camera measurements of the rising velocity proved successful in a laboratory and field setting, although some improvements to the setup are necessary to capture the air bubbles also at depths where little daylight penetrates.@en

Type: Pressure Sensor Keller DCX-4.0, SN 1466 This instrument is based on differential pressure measurement of two absolute sensors. The sensors measure pressure and temperature. Height of V-notch above sensor: -0.017m.@en

Explanation of the calibration procedure of the DTS derived temperatures in the Maisbich river, Luxembourg. Note that it only corrects offsets orriginating from the DTS device (Halo, Sensornet, UK). It does NOT correct for periods when the cable is not properly submerged in the stream. The DTS obtained temperatures were calibrated with five independent temperature loggers (TidbiT v2 Temp logger, HOBO, USA) located along the cable at 26, 395, 931, 1111 and 1270 m from the DTS desktop computer, respectively. Note that the investigated branch lies between 717 (V-notch weir Q3) and 1282 m (V-notch weir Q4) from the desktop computer. For each Tidbit temperature logger, which measured at a 6 min interval, a linear relation was determined between the DTS derived temperature and the difference between the TidbiT and DTS derived temperature for the period 4-Apr-2008 until 4-Dec-2008 (dT = a*T_DTS + b). For each Tidbit location a slope (a) and an offset (b) was determined: Tidbit Distance(m) a b T coolbox 26 -1.31 0,0974 T road 395 -0.49 0,0664 T Mai171u 931 -0.69 0,122 T Mai11u 1111 -0.25 0,109 T Q4-2 1270 -0.056 0,0965 Then a lineair relation between distance from the DTS desktop computer and slope (a_slope + b_slope*X), and between distance from desktop computer and offset (a_offset + b_offset*X) was derived, resulting in an offset as a function of distance and DTS derived temperatures. Subsequently, this offset was added to the initial DTS derived temperatures. Tcal = T_DTS + ( a_slope + b_slope*X)T_DTS + (a_offset + b_offset*X) with: a_slope :0.085 b_slope :1.78e-005 a_offset:-1.16 b_offset:8.03e-004 In the last step the distance was reversed, with the upstream V-notch weir Q4 at 0 m. Only the part between V-notch weirs Q4 and Q3 (at 565 m) are shown.@en

Type: Pressure Sensor Keller DCX-4.0, SN 1454 This instrument is based on differential pressure measurement of two absolute sensors. The sensors measure pressure and temperature. Height of V-notch above sensor: hV=0.145m. The discharge is calculated with the rating curve Q=C*(h-hV)^2.5 where C is a calibration constant and h results from the pressure difference (Willem Luxemburg, Martijn Westhoff).@en

Type: Casella Tipping Bucket 100000 E This instrument measures rainfall by counting the times a bucket tips over due to filling with rainfall. Aperture 400 cm3 (?) Bucket size 0.2mm (?)@en

Type: Pressure Sensor Keller DCX-4.0, SN 1454 This instrument is based on differential pressure measurement of two absolute sensors. The sensors measure pressure and temperature. Height of V-notch above sensor: hV=0.145m. The discharge is calculated with the rating curve Q=C*(h-hV)^2.5 where C is a calibration constant and h results from the pressure difference (Willem Luxemburg, Martijn Westhoff).@en

Type: Pressure Sensor Keller DCX-4.0, SN 1467 This instrument is based on differential pressure measurement of two absolute sensors. The sensors measure pressure and temperature. Height of V-notch above sensor: -0.0015m. Datasets with correction contain the corrected discharge observed at the Vnotch. The discharge is calculated as Q=Cd*h^2.5 After correcting the logger measurements with hand measured depths we interpolated the difference (dH) Then we determined the factor Cd with discharge measurements which we also interpolated The timing of hand measurements can be corrected to better represent the logged values The last corrections (variable "clean_corr") were made for blocking of the V-notch weir as folows: z1 represents the last good discharge value after which sedimentation slowly starts blocking the weir z2 represents the moment when the blocking is max z3 represents the moment just before cleaning of the weir z4 represents the moment after cleaning of the weir The max difference in discharge is Q(z3)-Q(z4)@en