Reservoirs play a vital role in the supply and management of water resources around the world. In Great Britain, reservoir operations are largely determined by the local water company, and there is very little national-scale information available to quantify their impact on river flows. Consequently, large-scale hydrological modelling and data analyses often focus on ‘natural’ or ‘near-natural’ catchments. To support the long-term resilience of water supply and the environment under changing climate and demand, it is essential that we understand where, when and how reservoirs are leading to deviations from a natural flow regime. This will help inform and validate advancements in the large-scale simulation of reservoir-impacted catchments, as well as deepening our understanding of how reservoir operations influence streamflow behaviour.Due to the age and location of reservoirs across Great Britain, there is a distinct lack of upstream or pre-construction flow timeseries. As a result, we cannot use standard approaches – such as indicators of hydrological alteration- which base analysis on the pre-and-post construction flow regimes. To fill this gap, we define a suite of hydrological signatures and compare observed streamflow timeseries from 1980- 2015 across 166 reservoir catchments and 112 near-natural catchments in Great Britain. We use signatures, characterizing the water balance, flow duration curve and low flow regime, to identify differences in streamflow between these two groups of catchments, and attribute alterations to upstream reservoir operation. We find that gauges with a reservoir upstream are more likely to induce runoff deficits exceeding total PET, and that routine reservoir releases lead to plateaus in the flow duration curve. By defining two new reservoir-based catchment descriptors, our results show that the degree of flow regulation at a gauge depends on the upstream storage capacity and the contributing area of upstream reservoirs. Such descriptors begin to identify thresholds below which the influence of reservoirs is indistinguishable, and help to characterise the extent of reservoir influence across Great Britain.This analysis highlights groups of reservoir-impacted catchments which cannot be represented by a natural regime. It is in these locations that advancements in large-scale hydrological modelling are crucial for water resource simulation, and that the influence of reservoir operations on the flow regime must be accounted for.

The GLUEX experiment at Jefferson Lab has been designed to study photoproduction reactions with a 9-GeV linearly polarized photon beam. The energy and arrival time of beam photons are tagged using a scintillator hodoscope and a scintillating fiber array. The photon flux is determined using a pair spectrometer, while the linear polarization of the photon beam is determined using a polarimeter based on triplet photoproduction. Charged-particle tracks from interactions in the central target are analyzed in a solenoidal field using a central straw-tube drift chamber and six packages of planar chambers with cathode strips and drift wires. Electromagnetic showers are reconstructed in a cylindrical scintillating fiber calorimeter inside the magnet and a lead-glass array downstream. Charged particle identification is achieved by measuring energy loss in the wire chambers and using the flight time of particles between the target and detectors outside the magnet. The signals from all detectors are recorded with flash ADCs and/or pipeline TDCs into memories allowing trigger decisions with a latency of 3.3 mu s. The detector operates routinely at trigger rates of 40 kHz and data rates of 600 megabytes per second. We describe the photon beam, the GLUEX detector components, electronics, data-acquisition and monitoring systems, and the performance of the experiment during the first three years of operation.

This study utilizes a rich UK data set of smart demand metering data, household characteristics, and weather data to develop a demand forecasting methodology that combines the high accuracy of machine learning models with the interpretability of statistical methods. For this reason, a random forest model is used to predict daily demands 1 day ahead for groups of properties (mean of 3.8 households/group) with homogenous characteristics. A variety of interpretable machine learning techniques [variable permutation, accumulated local effects (ALE) plots, and individual conditional expectation (ICE) curves] are used to quantify the influence of these predictors (temporal, weather, and household characteristics) on water consumption. Results show that when past consumption data are available, they are the most important explanatory factor. However, when they are not, a combination of household and temporal characteristics can be used to produce a credible model with similar forecasting accuracy. Weather input has overall a mild to no effect on the model's output, although this effect can become significant under certain conditions. (C) 2021 American Society of Civil Engineers.

In this paper we present the interactive Reservoir Operations Notebooks and Software (iRONS) toolbox for reservoir modelling and optimisation. The toolbox is meant to serve the research and professional community in hydrology and water resource management and contribute to bridge the gaps between them. iRONS is composed of a package of Python core functions and a set of interactive Jupyter Notebooks. Core functions implement typical reservoir modelling tasks and the interactive Jupyter Notebooks illustrate, with practical examples, the key functionalities of iRONS. We describe our development philosophy, the key features of iRONS, and report some results of evaluating the effectiveness of interactive Jupyter Notebooks for training and knowledge transfer. The paper may be of interest also beyond the water resources management field, as an example of how Jupyter Notebooks and interactive visualisation help improving the documentation and sharing of open-source code and the communication of underpinning methodologies.

Proper consideration of uncertainty has become a cornerstone of model-informed planning of water resource systems. In the UK Government’s 2020 Water Resources Planning Guidelines, the word “uncertainty” appears 48 times in 82 pages. This emphasis on uncertainty aligns with the increasing adoption by UK water companies of a “risk-based” approach to their long-term decision-making, in order to handle uncertainties in supply-demand estimation, climate change, population growth, etc. The term “risk-based” covers a range of methods - such as “info-gap”, “robust decision-making” or “system sensitivity analysis” - that come under different names but largely share a common rationale, essentially based on the use of Monte Carlo simulation. This shift in thinking from previous (deterministic) “worst-case” approach to a “risk-based” one is important and has the potential to significantly improve water resources planning practice. However its implementation is diminished by a certain lack of clarity about the terminology in use and about the concrete differences (and similarities) among methods. On top of these difficulties, in the next planning-cycle (2021-2026) two further step changes are introduced: (1) water companies are requested to move from a cost-efficiency approach focused on achieving the supply-demand balance, towards a fully multi-criteria approach that more explicitly encompasses other objectives including environmental sustainability; (2) as a further way to handle long-term uncertainties, they are required to embrace an “adaptive planning” approach. These changes will introduce two new sets of uncertainties around the robust quantification of criteria, particularly environmental ones, and around the attribution of weights to different criteria. This urgently calls for establishing structured approaches to quantify not only the uncertainty in model outputs, but also the sensitivity of those outputs to different forms of uncertainty in the modelling chain that mostly control the variability of the final outcome – the “best value” plan. Without this understanding of critical uncertainties, the risk is that huge efforts are invested on characterising and/or reducing uncertainties that later turn out to have little impact on the final outcome; or that water managers fall back to using oversimplified representation of those uncertainties as a way to escape the huge modelling burden. In this work, we aim at starting to establish a common rationale to “risk-based” methods within the context of a fully multi-criteria approach. We use a proof-of-concept example of a reservoir system in the South-West of England to demonstrate the use of global (i.e. Monte Carlo based) sensitivity analysis to simultaneously quantify output uncertainty and sensitivity, and identify robust decisions. We also discuss the potential of this approach to inform the construction of a “decision tree” for adaptive planning.

Understanding, comparing, and accurately predicting water demand at different spatial scales is an important goal that will allow effective targeting of the appropriate operational and conservation efforts under an uncertain future. This study uses data relating to water consumption available at the household level, as well as postcode locations, household characteristics, and weather data in order to identify the relationships between spatial scale, influencing factors, and forecasting accuracy. For this purpose, a Gradient Boosting Machine (GBM) is used to predict water demand 1-7 days into the future. Results show an exponential decay in prediction accuracy from a Mean Absolute Percentage Error (MAPE) of 3.2% to 17%, for a reduction in group size from 600 to 5 households. Adding explanatory variables to the forecasting model reduces the MAPE up to 20% for the peak days and smaller household groups (20-56 households), whereas for larger aggregations of properties (100-804 households), the range of improvement is much smaller (up to 1.2%). Results also show that certain types of input variables (past consumption and household characteristics) become more important for smaller aggregations of properties, whereas others (weather data) become less important.

Improved skill of long-range weather forecasts has motivated an increasing effort towards developing seasonal hydrological forecasting systems across Europe. Among other purposes, such forecasting systems are expected to support better water management decisions. In this paper we evaluate the potential use of a real-time optimization system (RTOS) informed by seasonal forecasts in a water supply system in the UK. For this purpose, we simulate the performances of the RTOS fed by ECMWF seasonal forecasting systems (SEAS5) over the past 10 years, and we compare them to a benchmark operation that mimics the common practices for reservoir operation in the UK. We also attempt to link the improvement of system performances, i.e. the forecast value, to the forecast skill (measured by the mean error and the continuous ranked probability skill score) as well as to the bias correction of the meteorological forcing, the decision maker priorities, the hydrological conditions and the forecast ensemble size. We find that in particular the decision maker priorities and the hydrological conditions exert a strong influence on the forecast skill–value relationship. For the (realistic) scenario where the decision maker prioritizes the water resource availability over energy cost reductions, we identify clear operational benefits from using seasonal forecasts, provided that forecast uncertainty is explicitly considered by optimizing against an ensemble of 25 equiprobable forecasts. These operational benefits are also observed when the ensemble size is reduced up to a certain limit. However, when comparing the use of ECMWF-SEAS5 products to ensemble streamflow prediction (ESP), which is more easily derived from historical weather data, we find that ESP remains a hard-to-beat reference, not only in terms of skill but also in terms of value.

Chemical Vapour Deposition (CVD) diamond is being considered as a material for particle detectors in a harsh radiation environment. This article presents beam test results of 3D pixel detectors fabricated with poly-crystalline CVD diamonds. The cells of the devices had a size of 50 mu m x 50 mu m with columns 2.6 mu m in diameter. The cells were ganged in a 3 x 2 and 5 x 1 pattern to match the layouts of the pixel read-out electronics currently used in the CMS and ATLAS experiments at the Large Hadron Collider, respectively. In beam tests, using tracks reconstructed with a high precision tracking telescope, a tracking efficiency of 99.3% was achieved. The efficiency of both devices plateaus at a bias voltage of 30V. Also irradiated poly-crystalline CVD diamond pad detectors were investigated. In high rate beam tests with particle fluxes up to 20 MHz/cm(2) and irradiations up to 8 . 10(15) n/cm(2) it was shown that the pulse height of irradiated poly-crystalline CVD diamonds does not depend on flux to the O(2%).

We have measured the radiation tolerance of poly-crystalline and single-crystalline diamonds grown by the chemical vapor deposition (CVD) process by measuring the charge collected before and after irradiation in a 50 mu m pitch strip detector fabricated on each diamond sample. We irradiated one group of sensors with 800 MeV protons, and a second group of sensors with 24 GeV protons, in steps, to (1.34 +/- 0.08 x 10(16)) protons cm(-2) and (1.80 +/- 0.18 x 10(16)) protons cm(-2) respectively. We observe the sum of mean drift paths for electrons and holes for both poly-crystalline CVD diamond and single-crystalline CVD diamond decreases with irradiation fluence from its initial value according to a simple damage curve characterized by a damage constant for each irradiation energy and the irradiation fluence. We find for each irradiation energy the damage constant, for poly-crystalline CVD diamond to be the same within statistical errors as the damage constant for single-crystalline CVD diamond. We find the damage constant for diamond irradiated with 24 GeV protons to be 0.62( -0.01)(+0.01) (stat) (+0.06)(-0.06) (syst) x 10(-18) cm(2) (p mu m)(-1) and the damage constant for diamond irradiated with 800 MeV protons to be 1.04 (+0.02)(-0.02) (stat) (-0.04)(-0.05) (syst) x 10(-18) cm(2) (p mu m)(-1) Moreover, we observe the FWHM/MP pulse height decreases with fluence for poly-crystalline CVD material and within statistical errors does not change with fluence for single-crystalline CVD material for both 24 GeV proton irradiation and 800 MeV proton irradiation. Finally, we have measured the uniformity of each sample as a function of fluence and observed that for poly-crystalline CVD diamond the samples become more uniform with fluence while for single-crystalline CVD diamond the uniformity does not change with fluence.

At present most experiments at the CERN Large Hadron Collider (LHC) are planning upgrades in the next 5-10 years for their innermost tracking layers as well as luminosity monitors to be able to take data as the luminosity increases and CERN moves toward the High Luminosity-LHC (HL-LHC). These upgrades will most likely require more radiation tolerant technologies than exist today. As a result this is one area of intense research, and Chemical Vapour Deposition (CVD) diamond is one such technology. CVD diamond has been used extensively in beam condition monitors as the innermost detectors in the highest radiation areas of all LHC experiments. This talk describes the preliminary radiation tolerance measurements of the highest quality polycrystalline CVD material for a range of proton energies and neutrons obtained with this material with the goal of elucidating the issues that should be addressed for future diamond based detectors. The talk presents the evolution of various semiconductor parameters as a function of dose.

Detectors based on Chemical Vapor Deposition (CVD) diamond have been used extensively and successfully in beam conditions/beam loss monitors as the innermost detectors in the highest radiation areas of Large Hadron Collider (LHC) experiments. The startup of the LHC in 2015 brought a new milestone where the first polycrystalline CVD (pCVD) diamond pixel modules were installed in an LHC experiment and successfully began operation. The RD42 collaboration at CERN is leading the effort to develop polycrystalline CVD diamond as a material for tracking detectors operating in extreme radiation environments. The status of the RD42 project with emphasis on recent beam test results is presented.

Predicting water demand is necessary to ensure a secure water supply to homes and businesses. With great uncertainty around future changes in the climate and in UK households, it is essential to accurately determine the effect of weather on water consumption. A systematic approach based on smart demand-metering data and customer characteristics (e.g., metering status and garden ownership) was used to investigate the sensitivity of household water consumption to weather, for different consumer types and time-varying parameters. The following weather variables were analyzed: air temperature, soil temperature, humidity, precipitation, and sunshine hours. Results indicated that the effect of the weather on water consumption is moderate in the UK. This effect was more significant for affluent customers with high monthly variations in consumption and medium-occupancy households; and during work days, summers, and evenings. Sunshine hours, humidity, and air temperature were the most influential weather variables. Soil temperature had a milder influence, whereas daily rainfall had minimal impact.

The design, simulation, fabrication, calibration, and performance of the GLUEX Start Counter detector are described. The Start Counter was designed to operate at integrated rates of up to 9 MHz with a timing resolution in the range of 500 to 825 ps (FWHM). The Start Counter provides excellent solid angle coverage, a high degree of segmentation for background rejection, and can be utilized in the level 1 trigger for the experiment. It consists of a cylindrical array of 30 thin scintillators with pointed ends that bend towards the beam line at the downstream end. Magnetic field insensitive silicon photomultiplier detectors were used as the light sensors.

Beam test results of the radiation tolerance study of chemical vapour deposition (CVD) diamond against different particle species and energies is presented. We also present beam test results on the independence of signal size on incident particle rate in charged particle detectors based on un-irradiated and irradiated poly-crystalline CVD diamond over a range of particle fluxes from 2 kHz/cm(2) to 10 MHz/cm(2). The pulse height of the sensors was measured with readout electronics with a peaking time of 6 ns. In addition functionality of poly-crystalline CVD diamond 3D devices was demonstrated in beam tests and 3D diamond detectors are shown to be a promising technology for applications in future high luminosity experiments.

AbstractIn this article, we reply to a comment made on our previous commentary regarding reproducibility in computational hydrology. Software licensing and version control of code are important technical aspects of making code and workflows of scientific experiments open and reproducible. However, in our view, it is the cultural change that is the greatest challenge to overcome to achieve reproducible scientific research in computational hydrology. We believe that from changing the culture and attitude among hydrological scientists, details will evolve to cover more (technical) aspects over time.

In this article, we reply to a comment made by Melsen et al. [2017] on our previous commentary regarding reproducibility in computational hydrology. Re‐executing someone else's code and workflow to derive a set of published results does not by itself constitute reproducibility. However, it forms a key part of the process: it demonstrates that all the degrees of freedom and choices made by the scientist in running the experiment are contained within that code and workflow. This does not only allow us to build and extend directly from the original work, but with full knowledge of decisions made in the original experimental setup, we can then focus our attention to the degrees of freedom of interest: those that occur in hydrological systems that are ultimately our subject of study.

Abstract. The core component of many hydrological systems, the moisture storage capacity available to vegetation, is impossible to observe directly at the catchment scale and is typically treated as a calibration parameter or obtained from a priori available soil characteristics combined with estimates of rooting depth. Often this parameter is considered to remain constant in time. This is not only conceptually problematic, it is also a potential source of error under the influence of land use and climate change. In this paper we test the potential of a recently introduced method to robustly estimate catchment-scale root zone storage capacities exclusively based on climate data (i.e. rainfall distribution and evaporation) to reproduce the temporal evolution of root zone storage under change. Using long-term data from three experimental catchments that underwent significant land use change, we tested the hypotheses that: (1) root zone moisture storage capacities are essentially controlled by land cover and climate, (2) root zone moisture storage capacities are dynamically adapting to changing environmental conditions, and (3) simple conceptual yet dynamic parametrization, mimicking changes in root zone storage capacities, can improve a model's skill to reproduce observed hydrological response dynamics. It was found that water-balance derived root zone storage capacities were similar to the values obtained from calibration of four different conceptual hydrological models. A sharp decline in root zone storage capacity was observed after deforestation, followed by a gradual recovery. Trend analysis suggested recovery periods between 5 and 13 years after deforestation. In a proof-of-concept analysis, one of the hydrological models was adapted to allow dynamically changing root zone storage capacities, following the observed changes due to deforestation. Although the overall performance of the modified model did not considerably change, it provided significantly better representations of high flows and peak flows, underlining the potential of the approach. In 54 % of all the evaluated hydrological signatures, considering all three catchments, improvements were observed when adding a time-variant representation of the root zone storage to the model. In summary, it is shown that root zone moisture storage capacities can be highly affected by deforestation and climatic influences and that a simple method exclusively based on climate-data can provide robust, catchment-scale estimates of this crucial and dynamic parameter.