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We present the first large-sample catchment hydrology dataset for Great Britain, CAMELS-GB (Catchment Attributes and MEteorology for Large-sample Studies). CAMELS-GB collates river flows, catchment attributes and catchment boundaries from the UK National River Flow Archive together with a suite of new meteorological time series and catchment attributes. These data are provided for 671 catchments that cover a wide range of climatic, hydrological, landscape, and human management characteristics across Great Britain. Daily time series covering 1970–2015 (a period including several hydrological extreme events) are provided for a range of hydro-meteorological variables including rainfall, potential evapotranspiration, temperature, radiation, humidity, and river flow. A comprehensive set of catchment attributes is quantified including topography, climate, hydrology, land cover, soils, and hydrogeology. Importantly, we also derive human management attributes (including attributes summarising abstractions, returns, and reservoir capacity in each catchment), as well as attributes describing the quality of the flow data including the first set of discharge uncertainty estimates (provided at multiple flow quantiles) for Great Britain. CAMELS-GB (Coxon et al., 2020; available at https://doi.org/10.5285/8344e4f3-d2ea-44f5-8afa-86d2987543a9) is intended for the community as a publicly available, easily accessible dataset to use in a wide range of environmental and modelling analyses.

Seasonality is ubiquitous in nature, and it is closely linked to water quality, ecology, hydrological extremes, and water resources management. Hydrological signatures aim at extracting information about certain aspects of hydrological behaviour. Commonly used seasonal hydro-climatological signatures consider climate or streamflow seasonality, but they do not consider how climate seasonality translates into streamflow seasonality. In order to analyse the translation of seasonal climate input (precipitation minus potential evapotranspiration) into seasonal catchment output (streamflow), we represent the two time series by their seasonal (annual) Fourier mode, i.e. by sine waves. A catchment alters the input sine wave by reducing its amplitude and by shifting its phase. We propose to use these quantities, the amplitude ratio and the phase shift, as seasonal hydrological signatures. We present analytical solutions describing the response of linear reservoirs to periodic forcing to interpret the seasonal signatures in terms of configurations of linear reservoirs. Using data from the UK and the US, we show that the seasonal signatures exhibit hydrologically interpretable patterns and that they are a function of both climate and catchment attributes. Wet, rather impermeable catchments hardly attenuate the seasonal climate input. Drier catchments, especially if underlain by a productive aquifer, strongly attenuate the input sine wave leading to phase shifts up to several months. As an example application, we test whether two commonly used hydrological models (Identification of unit Hydrographs and Component flows from Rainfall, Evaporation and Streamflow – IHACRES; modèle du Génie Rural à 4 paramètres Journalier – GR4J) can reproduce the observed ranges of seasonal signatures in the UK. The results show that the seasonal signatures have the potential to be useful for catchment classification, predictions in ungauged catchments, and model building and evaluation. The use of potential evapotranspiration in the input restricts the applicability of the signatures to energy-limited (humid) catchments.

This paper presents DECIPHeR (Dynamic fluxEs and ConnectIvity for Predictions of HydRology), a new model framework that simulates and predicts hydrologic flows from spatial scales of small headwater catchments to entire continents. DECIPHeR can be adapted to specific hydrologic settings and to different levels of data availability. It is a flexible model framework which includes the capability to (1) change its representation of spatial variability and hydrologic connectivity by implementing hydrological response units in any configuration and (2) test different hypotheses of catchment behaviour by altering the model equations and parameters in different parts of the landscape. It has an automated build function that allows rapid set-up across large model domains and is open-source to help researchers and/or practitioners use the model. DECIPHeR is applied across Great Britain to demonstrate the model framework. It is evaluated against daily flow time series from 1366 gauges for four evaluation metrics to provide a benchmark of model performance. Results show that the model performs well across a range of catchment characteristics but particularly in wetter catchments in the west and north of Great Britain. Future model developments will focus on adding modules to DECIPHeR to improve the representation of groundwater dynamics and human influences.

Chalk aquifers are an important source of drinking water in the UK. Due to their properties, they are particularly vulnerable to groundwater-related hazards like floods and droughts. Understanding and predicting groundwater levels is therefore important for effective and safe water management. Chalk is known for its high porosity and, due to its dissolvability, exposed to karstification and strong subsurface heterogeneity. To cope with the karstic heterogeneity and limited data availability, specialised modelling approaches are required that balance model complexity and data availability. In this study, we present a novel approach to evaluate simulated groundwater level frequencies derived from a semi-distributed karst model that represents subsurface heterogeneity by distribution functions. Simulated groundwater storages are transferred into groundwater levels using evidence from different observations wells. Using a percentile approach we can assess the number of days exceeding or falling below selected groundwater level percentiles. Firstly, we evaluate the performance of the model when simulating groundwater level time series using a spilt sample test and parameter identifiability analysis. Secondly, we apply a split sample test to the simulated groundwater level percentiles to explore the performance in predicting groundwater level exceedances. We show that the model provides robust simulations of discharge and groundwater levels at three observation wells at a test site in a chalk-dominated catchment in south-western England. The second split sample test also indicates that the percentile approach is able to reliably predict groundwater level exceedances across all considered timescales up to their 75th percentile. However, when looking at the 90th percentile, it only provides acceptable predictions for long time periods and it fails when the 95th percentile of groundwater exceedance levels is considered. By modifying the historic forcings of our model according to expected future climate changes, we create simple climate scenarios and we show that the projected climate changes may lead to generally lower groundwater levels and a reduction of exceedances of high groundwater level percentiles.

Rising nitrate concentrations in rivers and groundwater in regions with intensive agriculture have been of concern for several decades. Long records of nitrate concentration are rare; in the UK, few pre-date 1974. Records from the River Stour from 1937 are analysed using moving-windows to explore the effects of window width on trend detection and process inference. Nitrate concentrations rose sharply from the late 1950s in response to widespread ploughing of grassland, but have fallen since the early 1980s, suggesting that widespread nitrogen control measures, which were introduced after the start of the downward trend, may not be the principal driver for recent change. Short windows (5 years) are too noisy; longer windows (10, 15 and 30 years) identify signals (i.e. trends) more reliably. Since system response times can be of the order of 20–30 years, management decisions made on shorter data sets (even up to 15 years) could be misleading.

Long records of river water quality are invaluable for helping to understand the biogeochemistry of hydrological systems. They allow relationships to be established between changes in water quality (including seasonal cycles, episodic responses and long-term trends) and potential drivers, such as climatic forcing or human activity; they can act as a stimulus for process-oriented experimental research; they can be used to help to make predictions about future temporal and spatial patterns; and they can help to guide management options to mitigate water pollution. In this paper we present the case in favour of maintaining some long records of river water nitrate concentration at “benchmark” sites, in terms of enhancing process understanding and identifying system lags. Many long-term time series of nitrate concentration data share similar features including a pronounced seasonality characterised by a clear winter maximum, an upward trend in the post-war period followed by a levelling off, or even a decline in the last 20 years, and unusually high concentrations following drought years. Concentrations in any one year are often dependent on conditions in previous years; relationships can be established between concentrations and hydrological drivers (such as rainfall) with different lag periods which can yield information about supply or transport limitations to nitrate transfers. The interpretation of any record is dependent on its length: short records have a high potential for misinterpretation. Often, the value of long records only becomes apparent when analysed in retrospect, perhaps yielding insight into processes and phenomena for which the data collection programme was not originally designed. We, therefore, urge monitoring agencies to devise a strategy for maintaining long records – at least for a few benchmark stations. [ABSTRACT FROM AUTHOR]