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Accurately monitoring river flows can be challenging, particularly under high-flow conditions. In recent years, there has been considerable development of remote sensing techniques for the determination of river flow dynamics. Image velocimetry is one particular approach which has been shown to accurately reconstruct surface velocities under a range of hydro-geomorphic conditions. Building on these advances, a new software package, KLT-IV v1.0, has been designed to offer a user-friendly graphical interface for the determination of river flow velocity and river discharge using videos acquired from a variety of fixed and mobile platforms. Platform movement can be accounted for when ground control points and/or stable features are present or where the platform is equipped with a differential GPS device and inertial measurement unit (IMU) sensor. The application of KLT-IV v1.0 is demonstrated using two case studies at sites in the UK: (i) river Feshie and (ii) river Coquet. At these sites, footage is acquired from unmanned aerial systems (UASs) and fixed cameras. Using a combination of ground control points (GCPs) and differential GPS and IMU data to account for platform movement, image coordinates are converted to real-world distances and displacements. Flow measurements made with a UAS and fixed camera are used to generate a well-defined flow rating curve for the river Feshie. Concurrent measurements made by UAS and fixed camera are shown to deviate by < 4 % under high-flow conditions where maximum velocities exceed 3 m s−1. The acquisition of footage on the river Coquet using a UAS equipped with differential GPS and IMU sensors enabled flow velocities to be precisely reconstructed along a 180 m river reach. In-channel velocities of between 0.2 and 1 m s−1 are produced. Check points indicated that unaccounted-for motion in the UAS platform is in the region of 6 cm. These examples are provided to illustrate the potential for KLT-IV to be used for quantifying flow rates using videos collected from fixed or mobile camera systems.

While the availability and affordability of unmanned aerial systems (UASs) has led to the rapid development of remote sensing applications in hydrology and hydrometry, uncertainties related to such measurements are still to be quantified and mitigated. Physical instability of the UAS platform inevitably induces motion in the acquired videos and can have a significant impact on the accuracy of camera-based measurements such as velocimetry. A common practice in the data preprocessing stages is the compensation of platform-induced motion by means of digital image stabilisation (DIS) methods, which use the visual information from the captured videos – in the form of physically static features – to first estimate and then to compensate such motion. Most existing stabilisation approaches rely either on in-house built tools based on different algorithms, or on general-purpose commercial software. Intercomparison of different stabilisation tools for UAS remote sensing purposes that could serve as a basis for a selection of a particular tool in given conditions has not been found in the available literature. In this paper we have attempted to summarise and describe several freely available DIS tools applicable to UAS velocimetry purposes. A total of seven tools – six aimed specifically at velocimetry and one general purpose software – were investigated in terms of their (1) stabilisation accuracy in various conditions, (2) robustness, (3) computational complexity, and (4) user experience, using three case study videos with different flight and ground conditions. In attempt to adequately quantify the accuracy of the stabilisation using different tools, we have also presented a comparison metric based on root-mean-squared differences (RMSD) of interframe pixel intensities for selected static features. The most apparent differences between the investigated tools have been found with regards to the method for identifying and selecting static features in videos – manual selection of features or automatic. State-of-the-art methods which rely on automatic selection of features require fewer user-provided parameters and are able to select a significantly higher number of potentially static features (by several orders of magnitude) when compared to the methods which require manual identification of such features. This allows the former to achieve a higher stabilisation accuracy, but manual feature selection methods have demonstrated lower computational complexity and better robustness in complex field conditions. While this paper does not intend to identify the optimal stabilisation tool for UAS-based velocimetry purposes, it does aim to shed a light on implementational details which can help engineers and researchers choose the tool suitable for their needs and specific field conditions. Additionally, the RMSD comparison metric presented in this paper can also be used in order to measure the velocity estimation uncertainty induced by UAS motion.

Optical sensors coupled with image velocimetry techniques are becoming popular for river monitoring applications. In this context, new opportunities and challenges are growing for the research community aimed to: i) define standardized practices and methodologies; and ii) overcome some recognized uncertainty at the field scale. At this regard, the accuracy of image velocimetry techniques strongly depends on the occurrence and distribution of visible features on the water surface in consecutive frames. In a natural environment, the amount, spatial distribution and visibility of natural features on river surface are continuously challenging because of environmental factors and hydraulic conditions. The dimensionless seeding distribution index (SDI), recently introduced by Pizarro et al., 2020a,b and Dal Sasso et al., 2020, represents a metric based on seeding density and spatial distribution of tracers for identifying the best frame window (FW) during video footage. In this work, a methodology based on the SDI index was applied to different study cases with the Large Scale Particle Image Velocimetry (LSPIV) technique. Videos adopted are taken from the repository recently created by the COST Action Harmonious, which includes 13 case study across Europe and beyond for image velocimetry applications (Perks et al., 2020). The optimal frame window selection is based on two criteria: i) the maximization of the number of frames and ii) the minimization of SDI index. This methodology allowed an error reduction between 20 and 39% respect to the entire video configuration. This novel idea appears suitable for performing image velocimetry in natural settings where environmental and hydraulic conditions are extremely challenging and particularly useful for real-time observations from fixed river-gauged stations where an extended number of frames are usually recorded and analyzed. References Dal Sasso S.F., Pizarro A., Manfreda S., Metrics for the Quantification of Seeding Characteristics to Enhance Image Velocimetry Performance in Rivers. Remote Sensing, 12, 1789 (doi: 10.3390/rs12111789), 2020.Perks M. T., Dal Sasso S. F., Hauet A., Jamieson E., Le Coz J., Pearce S., …Manfreda S, Towards harmonisation of image velocimetry techniques for river surface velocity observations. Earth System Science Data, https://doi.org/10.5194/essd-12-1545-2020, 12(3), 1545 – 1559, 2020.Pizarro A., Dal Sasso S.F., Manfreda S., Refining image-velocimetry performances for streamflow monitoring: Seeding metrics to errors minimisation, Hydrological Processes, (doi: 10.1002/hyp.13919), 1-9, 2020.Pizarro A., Dal Sasso S.F., Perks M. and Manfreda S., Identifying the optimal spatial distribution of tracers for optical sensing of stream surface flow, Hydrology and Earth System Sciences, 24, 5173–5185, (10.5194/hess-24-5173-2020), 2020.

The hydrologic sciences and water resources management have long depended on a combination of in-situ measurements and remotely-sensed data for research and regulatory purposes. In-situ measurements are constrained in their spatial distribution by both logistics and costs. Satellite- and manned aircraft-based remote sensing provide spatially broad data, but often of a coarse resolution. Furthermore, all these data collection methods are often limited in their ability to respond to specific short-term events when the data and imagery could inform real-time assessment and decision-making, especially during and immediately after natural disasters. Researchers and regulators are increasingly using Unmanned Aerial Vehicles (UAVs) to fill these gaps in both spatial and temporal data resolution. UAVs are often more cost-effective than traditional remote sensing or in-situ data collection methodologies and the availability of new advancements in UAV design, power supply, payload capacity, and sensors has been driving rapid innovations in their use in the hydrological sciences. This in-depth review explores these UAV technologies and how they are being employed for on-demand and cost-effective characterization, monitoring, assessment, and modeling of soil and water resources. Our discussion addresses both key opportunities and challenges in surface water and groundwater studies, land-atmosphere interaction studies, water management in agriculture and forestry, ecosystem monitoring, and hydrological modeling.

While the availability and affordability of unmanned aerial systems (UASs) has led to the rapid development of remote sensing applications in hydrology and hydrometry, uncertainties related to such measurements must be quantified and mitigated. The physical instability of the UAS platform inevitably induces motion in the acquired videos and can have a significant impact on the accuracy of camera-based measurements, such as velocimetry. A common practice in data preprocessing is compensation of platform-induced motion by means of digital image stabilisation (DIS) methods, which use the visual information from the captured videos – in the form of static features – to first estimate and then compensate for such motion. Most existing stabilisation approaches rely either on customised tools developed in-house, based on different algorithms, or on general purpose commercial software. Intercomparison of different stabilisation tools for UAS remote sensing purposes that could serve as a basis for selecting a particular tool in given conditions has not been found in the literature. In this paper, we have attempted to summarise and describe several freely available DIS tools applicable to UAS velocimetry. A total of seven tools – six aimed specifically at velocimetry and one general purpose software – were investigated in terms of their (1) stabilisation accuracy in various conditions, (2) robustness, (3) computational complexity, and (4) user experience, using three case study videos with different flight and ground conditions. In an attempt to adequately quantify the accuracy of the stabilisation using different tools, we have also presented a comparison metric based on root mean squared differences (RMSDs) of inter-frame pixel intensities for selected static features. The most apparent differences between the investigated tools have been found with regards to the method for identifying static features in videos, i.e. manual selection of features or automatic. State-of-the-art methods which rely on automatic selection of features require fewer user-provided parameters and are able to select a significantly higher number of potentially static features (by several orders of magnitude) when compared to the methods which require manual identification of such features. This allows the former to achieve a higher stabilisation accuracy, but manual feature selection methods have demonstrated lower computational complexity and better robustness in complex field conditions. While this paper does not intend to identify the optimal stabilisation tool for UAS-based velocimetry purposes, it does aim to shed light on details of implementation, which can help engineers and researchers choose the tool suitable for their needs and specific field conditions. Additionally, the RMSD comparison metric presented in this paper can be used in order to measure the velocity estimation uncertainty induced by UAS motion.

River monitoring is of particular interest for our society that is facing increasing complexity in water management. Emerging technologies have contributed to opening new avenues for improving our monitoring capabilities, but also generating new challenges for the harmonised use of devices and algorithms. In this context, optical sensing techniques for stream surface flow velocities are strongly influenced by tracer characteristics such as seeding density and level of aggregation. Therefore, a requirement is the identification of how these properties affect the accuracy of such methods. To this aim, numerical simulations were performed to consider different levels of particle aggregation, particle colour (in terms of greyscale intensity), seeding density, and background noise. Two widely used image-velocimetry algorithms were adopted: i) Particle Tracking Velocimetry (PTV), and ii) Large-Scale Particle Image Velocimetry (LSPIV). A descriptor of the seeding characteristics (based on density and aggregation) was introduced based on a newly developed metric π. This value can be approximated and used in practice as π = ν0.1 / (ρ / ρcν1) where ν, ρ, and ρcν1 are the aggregation level, the seeding density, and the converging seeding density at ν = 1, respectively. A reduction of image-velocimetry errors was systematically observed by decreasing the values of π; and therefore, the optimal frame window was defined as the one that minimises π. In addition to numerical analyses, the Basento field case study (located in southern Italy) was considered as a proof-of-concept of the proposed framework. Field results corroborated numerical findings, and an error reduction of about 15.9 and 16.1 % was calculated – using PTV and PIV, respectively – by employing the optimal frame window.

Image velocimetry (IV) is a remote technique which calculates surface flow velocities of rivers (or fluids) via a range of cross-correlation and tracking algorithms. IV can be implemented via a range of camera sensors which can be mounted on tri-pods, or Unmanned Aerial Systems (UAS). IV has proven a powerful technique for monitoring river flows during flood conditions, whereby traditional in-situ techniques would be unsafe to deploy. However, little research has focussed upon the application of such techniques during low flow conditions. The applicability of IV to low flow studies could aid data collection at a higher spatial and temporal resolution than is currently available. Many IV techniques are under-development, that utilise different cross-correlation and tracking algorithms, including, Large Scale Particle Image Velocimetry (LSPIV), Large Scale Particle Tracking Velocimetry (LSPTV), Optical Tracking Velocimetry (OTV), Kanade Lucas Tomasi Image Velocimetry (KLT-IV) and Surface Structure Image Velocimetry (SSIV). Nevertheless, the true applications and limitations of such algorithms have yet to be extensively tested. Therefore, this study aimed to conduct a sensitivity analysis on the commonly relatable parameters between the different algorithms, including the particle identification area parameters (such as Interrogation Area (LSPIV, LSPTV and SSIV), Block Size (KLT-IV) and Trajectory Length (OTV)) and the feature extraction rate (or sub sampled frame rate).Fieldwork was carried out on Kolubara River near the city of Obrenovac in Central Serbia. Cross-sectional surface width was relatively constant, varying between 23.30 and 23.45m. During the experiment, low flow conditions were present with a discharge of approx. 3.4m3 s-1 (estimated using a Sontek M9 ADCP), and depths of up to 1.9m. A DJI Phantom 4 Pro UAS was used to collect video data of the surface flow. Artificial seeding material (wood-mulch) was distributed homogenously across the rivers’ surface, in order to improve the conditions for IV techniques during slow flows. Two 30-second videos were utilised for surface velocity analysis.This study highlighted that KLT, SSIV, OTV and LSPIV are the least sensitive algorithms to changing parameters when no pre- or post-processing of results are conducted. On the other hand, LSPTV must undergo post-processing procedures in order to avoid spurious results and only then, results may be reliable. Furthermore, KLT and SSIV highlighted a slight sensitivity to changing the feature extraction rate, however changing the particle identification area did not affect significantly the outputted surface velocity results. OTV and LSPTV, on the other hand, highlighted that changing the particle identification area provided a higher variability in the results, whilst changing the feature extraction rate did not affect the surface velocity outputs. LSPIV proved to be sensitive to changing both the feature extraction rate and the particle identification area.This analysis has led to the conclusions that during the conditions of sampling with surface velocities of approximately 0.12ms-1, and homogeneous seeding on the rivers surface, IV techniques can provide results comparable to traditional techniques such as ADCPs during low flow conditions. All IV algorithms provided results that were, on average, within 0.05ms-1 of the ADCP measurements.

Image velocimetry has proven to be a promising technique for monitoring river flows using remotely operated platforms such as Unmanned Aerial Systems (UAS). However, the application of various image velocimetry algorithms has not been extensively assessed. Therefore, a sensitivity analysis has been conducted on five different image velocimetry algorithms including Large Scale Particle Image Velocimetry (LSPIV), Large-Scale Particle Tracking Velocimetry (LSPTV), Kanade–Lucas Tomasi Image Velocimetry (KLT-IV or KLT), Optical Tracking Velocimetry (OTV) and Surface Structure Image Velocimetry (SSIV), during low river flow conditions (average surface velocities of 0.12–0.14 m s − 1 , Q60) on the River Kolubara, Central Serbia. A DJI Phantom 4 Pro UAS was used to collect two 30-second videos of the surface flow. Artificial seeding material was distributed homogeneously across the rivers surface, to enhance the conditions for image velocimetry techniques. The sensitivity analysis was performed on comparable parameters between the different algorithms, including the particle identification area parameters (such as Interrogation Area (LSPIV, LSPTV and SSIV), Block Size (KLT-IV) and Trajectory Length (OTV)) and the feature extraction rate. Results highlighted that KLT and SSIV were sensitive to changing the feature extraction rate; however, changing the particle identification area did not affect the surface velocity results significantly. OTV and LSPTV, on the other hand, highlighted that changing the particle identification area presented higher variability in the results, while changing the feature extraction rate did not affect the surface velocity outputs. LSPIV proved to be sensitive to changing both the feature extraction rate and the particle identification area. This analysis has led to the conclusions that for surface velocities of approximately 0.12 m s − 1 image velocimetry techniques can provide results comparable to traditional techniques such as ADCPs. However, LSPIV, LSPTV and OTV require additional effort for calibration and selecting the appropriate parameters when compared to KLT-IV and SSIV. Despite the varying levels of sensitivity of each algorithm to changing parameters, all configuration image velocimetry algorithms provided results that were within 0.05 m s − 1 of the ADCP measurements, on average.

ISI Document Delivery No.: FV7JXTimes Cited: 4Cited Reference Count: 249Tauro, Flavia Selker, John van de Giesen, Nick Abrate, Tommaso Uijlenhoet, Remko Porfiri, Maurizio Manfreda, Salvatore Caylor, Kelly Moramarco, Tommaso Benveniste, Jerome Ciraolo, Giuseppe Estes, Lyndon Domeneghetti, Alessio Perks, Matthew T. Corbari, Chiara Rabiei, Ehsan Ravazzani, Giovanni Bogena, Heye Harfouche, Antoine Brocca, Luca Maltese, Antonino Wickert, Andy Tarpanelli, Angelica Good, Stephen Alcala, Jose Manuel Lopez Petroselli, Andrea Cudennec, Christophe Blume, Theresa Hut, Rolf Grimaldi, SalvatoreTaylor & francis ltdAbingdon; To promote the advancement of novel observation techniques that may lead to new sources of information to help better understand the hydrological cycle, the International Association of Hydrological Sciences (IAHS) established the Measurements and Observations in the XXI century (MOXXI) Working Group in July 2013. The group comprises a growing community of tech-enthusiastic hydrologists that design and develop their own sensing systems, adopt a multi-disciplinary perspective in tackling complex observations, often use low-cost equipment intended for other applications to build innovative sensors, or perform opportunistic measurements. This paper states the objectives of the group and reviews major advances carried out by MOXXI members toward the advancement of hydrological sciences. Challenges and opportunities are outlined to provide strategic guidance for advancement of measurement, and thus discovery.

Recent surface-water and flash floods have caused millions of pounds worth of damage in the UK. These events form rapidly and are difficult to predict due to their short-lived and localised nature. The interdisciplinary Flooding From Intense Rainfall (FFIR) programme investigated the feasibility of enhancing the integration of an end-to-end forecasting system for flash and surface-water floods to help increase the lead time for warnings for these events. Here we propose developments to the integration of an operational end-to-end forecasting system based on the findings of the FFIR programme. The suggested developments include methods to improve radar-derived rainfall rates and understanding of the uncertainty in the position of intense rainfall in weather forecasts; the addition of hydraulic modelling components; and novel education techniques to help lead to effective dissemination of flood warnings. We make recommendations for future advances such as research into the propagation of uncertainty throughout the forecast chain. We further propose the creation of closer bonds to the end users to allow for an improved, integrated, end-to-end forecasting system that is easily accessible for users and end users alike, and will ultimately help mitigate the impacts of flooding from intense rainfall by informed and timely action.

Acquisition of real-time hydraulic data is an essential component for flood forecasting. However, we frequently face difficulties in obtaining discharge data using classical contact methods during high magnitude floods and for systems experiencing rapid hydro-geomorphological adjustment. Therefore, we developed low-cost, non-contact sensors and platforms that are designed to overcome these difficulties. These advances enable flood flow properties to be monitored at multiple locations across a river catchment, at low-cost, and communicated in near real-time by using an image velocimetry method. This is an optics-based approach for stream flow measurement using commercially available near-infrared digital cameras to acquire video footage in full HD (30fps). Video footage is then subjected to optical flow tracking techniques based on cross-correlation, and feature-based tracking, enabling the displacement rates of detected features (for example natural foam, seeds, woody debris, and turbulent structures) to be computed. This manual provides step by step guidance to install an image-based gauging station. It contains the list of necessary components, the calibration process of a new camera and the assembly procedure of the system.

Small unmanned aerial systems (UASs) equipped with an optical camera are a cost-effective strategy for topographic surveys. These low-cost UASs can provide useful information for three-dimensional (3D) reconstruction even if they are equipped with a low-quality navigation system. To ensure the production of high-quality topographic models, careful consideration of the flight mode and proper distribution of ground control points are required. To this end, a commercial UAS was adopted to monitor a small earthen dam using different combinations of flight configurations and by adopting a variable number of ground control points (GCPs). The results highlight that optimization of both the choice and combination of flight plans can reduce the relative error of the 3D model to within two meters without the need to include GCPs. However, the use of GCPs greatly improved the quality of the topographic survey, reducing error to the order of a few centimeters. The combined use of images extracted from two flights, one with a camera mounted at nadir and the second with a 20&deg, angle, was found to be beneficial for increasing the overall accuracy of the 3D model and especially the vertical precision.

Extreme rainfall events are predicted to become more frequent with climate change and can have a major bearing on instream solute and pollutant transport in mineralised catchments. The Coledale Beck catchment in north-west England was subject to an extreme rainfall event in December 2015 that equated to a 1 in 200-year event. The catchment contains the UK's first passive metal mine water treatment system, and as such had been subject to intense monitoring of solute dynamics before and after commissioning. Due to this monitoring record, the site provides a unique opportunity to assess the effects of a major storm event on (1) catchment-scale solute transport, and (2) the resilience of the new and novel passive treatment system to extreme events. Monitoring suggests a modest decline in treatment efficiency over time that is not synchronous with the storm event and explained instead by changes in system hydraulic efficiency. There was no apparent flushing of the mine system during the event that could potentially have compromised treatment system performance. Analysis of metal transport in the catchment downstream of the mine suggests relatively subtle changes in instream chemistry with modest but statistically-significant reductions in zinc in the lower catchment irrespective of flow condition after the extreme event, but most parameters of interest show no significant change. Increased export of colloidal iron and aluminium is associated with major landslips in the mid-catchment after the storm and provide fresh sorption sites to attenuate dissolved zinc more rapidly in these locations, corroborated by laboratory experiments utilising site materials to investigate the attenuation/release of metals from stream and terrestrial sediments. The data are important as they show both the resilience of passive mine water treatment systems to extreme events and the importance of catchment-scale monitoring to ensure continued effectiveness of treatment initiatives after major perturbation.

This paper describes the implementation of a novel mitigation approach and subsequent adaptive management, designed to reduce the transfer of fine sediment (−1 prior to mitigation, to 74.36 mg L−1 following the diversion. A Mann–Whitney U test found statistically significant differences (p p a and b coefficients becoming smaller following the diversion. Non-parametric analysis indicates a reduction in residuals through time (p

Small unmanned aerial systems (UAS) represent a cost-effective strategy for topographic surveys. These low-cost drones can provide useful information for 3D reconstruction even if they are equipped with a low-quality navigation system. To ensure the production of high-quality topographic models, careful consideration of flight mode and proper distribution of ground control points is required. To this end a commercial drone has been adopted to monitor a small earthen dam using different combinations of flight configurations and adopting a variable number of ground control points (GCPs). Results highlighted that both choice and combination of flight plans can reduce the relative error of the 3D model up to a few meters without the need of including GCPs. The use of GCPs allows the quality of topographic survey to be greatly improved, reducing error to the order of a few centimeters. In particular, the combined use of images extracted from two flights, one with a camera mounted at nadir and the second with a 20° angle, proves extremely beneficial to increase the overall accuracy of the 3D model and especially of the vertical precision.

[EN] Environmental monitoring plays a central role in diagnosing climate and management impacts on natural and agricultural systems; enhancing the understanding of hydrological processes; optimizing the allocation and distribution of water resources; and assessing, forecasting, and even preventing natural disasters. Nowadays, most monitoring and data collection systems are based upon a combination of ground-based measurements, manned airborne sensors, and satellite observations. These data are utilized in describing both small-and large-scale processes, but have spatiotemporal constraints inherent to each respective collection system. Bridging the unique spatial and temporal divides that limit current monitoring platforms is key to improving our understanding of environmental systems. In this context, Unmanned Aerial Systems (UAS) have considerable potential to radically improve environmental monitoring. UAS-mounted sensors offer an extraordinary opportunity to bridge the existing gap between field observations and traditional air-and space-borne remote sensing, by providing high spatial detail over relatively large areas in a cost-effective way and an entirely new capacity for enhanced temporal retrieval. As well as showcasing recent advances in the field, there is also a need to identify and understand the potential limitations of UAS technology. For these platforms to reach their monitoring potential, a wide spectrum of unresolved issues and application-specific challenges require focused community attention. Indeed, to leverage the full potential of UAS-based approaches, sensing technologies, measurement protocols, postprocessing techniques, retrieval algorithms, and evaluation techniques need to be harmonized. The aim of this paper is to provide an overview of the existing research and applications of UAS in natural and agricultural ecosystem monitoring in order to identify future directions, applications, developments, and challenges., The present work has been funded by the COST Action CA16219 "HARMONIOUS-Harmonization of UAS techniques for agricultural and natural ecosystems monitoring". B. Toth acknowledges financial support by the Hungarian National Research, Development and Innovation Office (NRDI) under grant KH124765. J. Millerovd was supported by projects GA17-13998S and RVO67985939. Isabel and Jodo de Lima were supported by project HIRT (PTDC/ECM-HID/4259/2014-POCI-0145-FEDER016668).

Under the EU Water Framework Directive, suspended sediment is omitted from environmental quality standards and compliance targets. This omission is partly explained by difficulties in assessing the complex dose-response of ecological communities. But equally, it is hindered by a lack of spatially distributed estimates of suspended sediment variability across catchments. In this paper, we demonstrate the inability of traditional, discrete sampling campaigns for assessing exposure to fine sediment. Sampling frequencies based on Environmental Quality Standard protocols, whilst reflecting typical manual sampling constraints, are unable to determine the magnitude of sediment exposure with an acceptable level of precision. Deviations from actual concentrations range between −35 and +20% based on the interquartile range of simulations. As an alternative, we assess the value of low-cost, suspended sediment sampling networks for quantifying suspended sediment transfer (SST). In this study of the 362 km2 upland Esk catchment we observe that spatial patterns of sediment flux are consistent over the two year monitoring period across a network of 17 monitoring sites. This enables the key contributing sub-catchments of Butter Beck (SST: 1141 t km2 yr−1) and Glaisdale Beck (SST: 841 t km2 yr−1) to be identified. The time-integrated samplers offer a feasible alternative to traditional infrequent and discrete sampling approaches for assessing spatio-temporal changes in contamination. In conjunction with a spatially distributed diffuse pollution model (SCIMAP), time-integrated sediment sampling is an effective means of identifying critical sediment source areas in the catchment, which can better inform sediment management strategies for pollution prevention and control.

Headwater streams are a central feature of the landscape, with their diversity in structure and associated ecological function providing a potential natural buffer against downstream nutrient export. Phytobenthic communities, dominated in many headwaters by diatoms, must respond to physical and chemical parameters that can vary in magnitude within hours whereas the ecological regeneration times are much longer. How diatom communities develop in the fluctuating, dynamic environments characteristic of headwaters is poorly understood. Deployment of monitoring technology in sub-catchments of the River Eden, NW England, provides the opportunity for near-continuous measurement of temporal variability in stream discharge and nutrient resource supply to benthic communities, as represented by monthly diatom samples collected over two years. Our data suggest that the diatom communities and the derived Trophic Diatom Index, best reflect stream discharge conditions over the preceding 15 - 21 days and TP concentrations over a wider antecedent window of 7 – 21 days. This is one of the first quantitative assessments of longterm diatom community development in response to continuously-measured stream nutrient concentration and discharge fluctuations. The data reveal the sensitivity of these headwater communities to mean conditions prior to sampling, with flow as the dominant variable. With sufficient understanding of the role of antecedent conditions, these methods Page 3 of 18 Environmental Science: Processes & Impacts can be used to inform interpretation of monitoring data, including those collected under the European Water Framework Directive and related mitigation efforts.

Delivery of fine sediment to fluvial systems is of considerable concern given the physical and ecological impacts of elevated levels in drainage networks. Although it is possible to measure the transfer of fine sediment at high frequency by using a range of surrogate and automated technologies, the demands for assessing sediment flux and sediment properties at multiple spatially distributed locations across catchments can often not be met using established sampling techniques. The time-integrated mass-flux sampler (TIMS) has the potential to bridge this gap and further our understanding of fine sediment delivery in fluvial systems. However, these devices have undergone limited testing in the field. The aim of this paper was to provide a critical validation of TIMS as a technique for assessing fluvial fine sediment transfer. Fine sediment flux and sediment properties were assessed over 2 years with individual sampling periods of approximately 30 days. Underestimation of sediment flux ranged between 66% and 99% demonstrating that TIMS is unsuitable for assessing absolute sediment loads. However, assessment of relative efficiency showed that six of seven samplers produced statistically strong relationships with the reference sediment load (P

UAVs have the potential to capture information about the earth's surface in dangerous and previously inaccessible locations. Through image acquisition of flash flood events and subsequent object-based analysis, highly dynamic and oft- immeasurable hydraulic phenomenon may be quantified at previously unattainable spatial and temporal resolutions. The potential for this approach to provide valuable information about the hydraulic conditions present during dynamic, high-energy flash floods has until now not been explored. In this paper we adopt a novel approach, utilising the Kande–Lucas–Tomasi (KLT) algorithm to track features present on the water surface which are related to the free-surface velocity. Following the successful tracking of features, a method analogous to the vector correction method has enabled accurate geometric rectification of velocity vectors. Uncertainties associated with the rectification process induced by unsteady camera movements are subsequently explored. Geo-registration errors are relatively stable and occur as a result of persistent residual distortion effects following image correction. The apparent ground movement of immobile control points between measurement intervals ranges from 0.05–0.13 m. The application of this approach to assess the hydraulic conditions present in Alyth Burn, Scotland during a 1 : 200 year flash flood resulted in the generation of an average 4.2 measurements m−2 at a rate of 508 measurements s−1. Analysis of these vectors provide a rare insight into the complexity of channel-overbank interactions during flash floods. The uncertainty attached to the calculated velocities is relatively low with a spatial average across the area of &pm;0.15 m s−1. Little difference is observed in the uncertainty attached to out-of-bank velocities (&pm;0.15 m s−1), and within-channel velocities (&pm;0.16 m s−1), illustrating the consistency of the approach.

The water quality of our rivers and lakes is a reflection of the landscape over and through which it travels. The UK government, along with all European Union member states, are obliged under the Water Framework Directive (WFD) to aim for good ecological status of fresh water bodies by 2015. In order to evaluate the effectiveness of potential mitigation measures in reducing diffuse water pollution from agriculture at the catchment scale, the Demonstration Test Catchment (DTC) project was developed. The project is jointly funded by Defra, the Environment Agency (EA) and the Welsh Assembly Government (WAG). There are three DTCs across the country: the Eden catchment, Cumbria; the Wensum catchment, Norfolk and the Hampshire Avon catchment. The Eden DTC has established three ∼10 km2 focus catchments, chosen to reflect different farming practices, geologies, elevations and hydrological characteristics. Within each focus catchment, two sub-catchments have been selected, one control and one mitigated, in which numerous existing and novel mitigation measures will be tested. It is hoped that the mitigation features will be multi-purpose, having positive effects on pollutant retention, flooding, carbon sequestration, habitat creation and biodiversity. The effectiveness of these measures is assessed through networks of hydro-meteorological and water-quality instrumentation, most of which will provide data in near real time, with sub-hourly time steps.

High resolution water quality data has recently become widely available from numerous catchment based monitoring schemes. However, the models that can reproduce time series of concentrations or fluxes have not kept pace with the advances in monitoring data. Model performance at predicting phosphorus (P) and sediment concentrations has frequently been poor with models not fit for purpose except for predicting annual losses. Here, the data from the Eden Demonstration Test Catchments (DTC) project have been used to calibrate the Catchment Runoff Attenuation Flux Tool (CRAFT), a new, parsimonious model developed with the aim of modelling both the generation and attenuation of nutrients and sediments in small to medium sized catchments. The CRAFT has the ability to run on an hourly timestep and can calculate the mass of sediments and nutrients transported by three flow pathways representing rapid surface runoff, fast subsurface drainage and slow groundwater flow (baseflow). The attenuation feature of the model is introduced here; this enables surface runoff and contaminants transported via this pathway to be delayed in reaching the catchment outlet. It was used to investigate some hypotheses of nutrient and sediment transport in the Newby Beck Catchment (NBC) Model performance was assessed using a suite of metrics including visual best fit and the Nash-Sutcliffe efficiency. It was found that this approach for water quality models may be the best assessment method as opposed to using a single metric. Furthermore, it was found that, when the aim of the simulations was to reproduce the time series of total P (TP) or total reactive P (TRP) to get the best visual fit, that attenuation was required. The model will be used in the future to explore the impacts on water quality of different mitigation options in the catchment; these will include attenuation of surface runoff.

This paper uses high-frequency bankside measurements from three catchments selected as part of the UK government-funded Demonstration Test Catchments (DTC) project. We compare the hydrological and hydrochemical patterns during the water year 2011–2012 from the Wylye tributary of the River Avon with mixed land use, the Blackwater tributary of the River Wensum with arable land use and the Newby Beck tributary of the River Eden with grassland land use. The beginning of the hydrological year was unusually dry and all three catchments were in states of drought. A sudden change to a wet summer occurred in April 2012 when a heavy rainfall event affected all three catchments. The year-long time series and the individual storm responses captured by in situ nutrient measurements of nitrate and phosphorus (total phosphorus and total reactive phosphorus) concentrations at each site reveal different pollutant sources and pathways operating in each catchment. Large storm-induced nutrient transfers of nitrogen and or phosphorus to each stream were recorded at all three sites during the late April rainfall event. Hysteresis loops suggested transport-limited delivery of nitrate in the Blackwater and of total phosphorus in the Wylye and Newby Beck, which was thought to be exacerbated by the dry antecedent conditions prior to the storm. The high rate of nutrient transport in each system highlights the scale of the challenges faced by environmental managers when designing mitigation measures to reduce the flux of nutrients to rivers from diffuse agricultural sources. It also highlights the scale of the challenge in adapting to future extreme weather events under a changing climate.

In this paper we employ rhetoric culture theory, and a case study of upland channel truncation in the UK, to explore the nuanced processes of negotiation associated with environmental decision-making. In contrast to much of the literature on rhetoric in environmental management, which focuses on the means by which decisions are communicated and justified to an external audience, we focus on the dynamics of interaction and persuasion in and amongst a small group of decision-makers, and how, despite initial misgivings and conflict, they arrived at a decision consensus. We reflect on the importance of the rhetorical situation as a determinant of action and demonstrate how antagonisms were caused by competing moral notions of environmental restoration. We show that consensus was finally achieved through a process of divergent reframing, as individuals reframed the problem according to their own prior values. The outcome, therefore, was a consensus of action but a divergence of opinion, which sheds new light on the role of reframing in environmental management. Finally, we argue for a better understanding of how nuanced interactional processes influence not only small-scale interventions, but all environmental decision processes.