The future for global water assessment

The global water cycle is a fundamental component of our climate and Earth system. Many, if not the majority, of the impacts of climate change are water related. We have an imperfect description and understanding of components of the water cycle. This arises from an incomplete observation of some of the stores and fluxes in the water cycle (in particular: precipitation, evaporation, soil moisture and groundwater), problems with the simulation of precipitation by global climate models and the wide diversity of global hydrological models currently in use. This paper discusses these sources of errors and, in particular, explores the errors and advantages of bias correcting climate model outputs for hydrological models using a single large catchment as an example (the Rhine). One conclusion from this analysis is that bias correction is necessary and has an impact on the mean flows and their seasonal cycle. However choice of hydrological model has an equal, if not larger effect on the quality of the simulation. The paper highlights the importance of improving hydrological models, which run at a continental and global scale, and the importance of quantifying uncertainties in impact studies

Integrated stratigraphy of the Kimmeridge Clay Formation (Upper Jurassic) based on exposures and boreholes in south Dorset, UK

For the purposes of a high-resolution multi-disciplinary study of the Upper Jurassic Kimmeridge Clay Formation, two boreholes were drilled at Swanworth Quarry and one at Metherhills, south Dorset, UK. Together, the cores represent the first complete section through the entire formation close to the type section. We present graphic logs that record the stratigraphy of the cores, and outline the complementary geophysical and analytical data sets (gamma ray, magnetic susceptibility, total organic carbon, carbonate, [delta]13Corg). Of particular note are the new borehole data from the lowermost part of the formation which does not crop out in the type area. Detailed logs are available for download from the Kimmeridge Drilling Project web-site at http://kimmeridge.earth.ox.ac.uk/. Of further interest is a mid-eudoxus Zone positive shift in the [delta]13Corg record, a feature that is also registered in Tethyan carbonate successions, suggesting that it is a regional event and may therefore be useful for correlation. The lithostratigraphy of the cores has been precisely correlated with the nearby cliff section, which has also been examined and re-described. Magnetic-susceptibility and spectral gamma-ray measurements were made at a regular spacing through the succession, and facilitate core-to-exposure correlation. The strata of the exposure and core have been subdivided into four main mudrock lithological types: (a) medium-dark–dark-grey marl; (b) medium-dark–dark grey–greenish black shale; (c) dark-grey–olive-black laminated shale; (d) greyish-black–brownish-black mudstone. The sections also contain subordinate amounts of siltstone, limestone and dolostone. Comparison of the type section with the cores reveals slight lithological variation and notable thickness differences between the coeval strata. The proximity of the boreholes and different parts of the type section to the Purbeck–Isle of Wight Disturbance is proposed as a likely control on the thickness changes

Exploiting the weekly cycle as observed over Europe to analyse aerosol indirect effects in two climate models: Exploiting the weekly cycle as observed over Europe to analyseaerosol indirect effects in two climate models

A weekly cycle in aerosol pollution and some meteorological quantities is observed over Europe. In the present study we exploit this effect to analyse aerosol-cloudradiation interactions. A weekly cycle is imposed on anthropogenic emissions in two general circulation models that include parameterizations of aerosol processes and cloud microphysics. It is found that the simulated weekly cycles in sulfur dioxide, sulfate, and aerosol optical depth in both models agree reasonably well with those observed indicating model skill in simulating the aerosol cycle. A distinct weekly cycle in cloud droplet number concentration is demonstrated in both observations and models. For other variables, such as cloud liquid water path, cloud cover, top-of-the-atmosphere radiation fluxes, precipitation, and surface temperature, large variability and contradictory results between observations, model simulations, and model control simulations without a weekly cycle in emissions prevent us from reaching any firm conclusions about the potential aerosol impact on meteorology or the realism of the modelled second aerosol indirect effects

Exploiting the weekly cycle as observed over Europe to analyse aerosol indirect effects in two climate models: Exploiting the weekly cycle as observed over Europe to analyseaerosol indirect effects in two climate models

A weekly cycle in aerosol pollution and some meteorological quantities is observed over Europe. In the present study we exploit this effect to analyse aerosol-cloudradiation interactions. A weekly cycle is imposed on anthropogenic emissions in two general circulation models that include parameterizations of aerosol processes and cloud microphysics. It is found that the simulated weekly cycles in sulfur dioxide, sulfate, and aerosol optical depth in both models agree reasonably well with those observed indicating model skill in simulating the aerosol cycle. A distinct weekly cycle in cloud droplet number concentration is demonstrated in both observations and models. For other variables, such as cloud liquid water path, cloud cover, top-of-the-atmosphere radiation fluxes, precipitation, and surface temperature, large variability and contradictory results between observations, model simulations, and model control simulations without a weekly cycle in emissions prevent us from reaching any firm conclusions about the potential aerosol impact on meteorology or the realism of the modelled second aerosol indirect effects

Quantifying land surface temperature variability for two Sahelian mesoscale regions during the wet season

Land-atmosphere feedbacks play an important role in the weather and climate of many semi-arid regions. These feedbacks are strongly controlled by how the surface responds to precipitation events, which regulate the return of heat and moisture to the atmosphere. Characteristics of the surface can result in both differing amplitudes and rates of warming following rain. We used spectral analysis to quantify these surface responses to rainfall events using land surface temperature (LST) derived from Earth Observations (EO). We analysed two mesoscale regions in the Sahel and identified distinct differences in the strength of the short-term (< 5–day) spectral variance, notably a shift towards lower frequency variability in forest pixels relative to non-forest areas, and an increase in amplitude with decreasing vegetation cover. Consistent with these spectral signatures, we found that areas of forest, and to a lesser extent grassland regions, warm up more slowly than sparsely vegetated or barren pixels. We applied the same spectral analysis method to simulated LST data from the the Joint UK Land Environment Simulator (JULES) land surface model. We found a reasonable level of agreement with the EO spectral analysis, for two contrasting land surface regions. However JULES shows a significant underestimate in the magnitude of the observed response to rain compared to EO. A sensitivity analysis of the JULES model highlights an unrealistically high level of soil water availability as a key deficiency, which dampens the models response to rainfall events

Leaf phenology amplitude derived from MODIS NDVI and EVI: maps of leaf phenology synchrony for Meso‐ and South America

The leaf phenology (i.e. the seasonality of leaf amount and leaf demography) of ecosystems can be characterized through the use of Earth observation data using a variety of different approaches. The most common approach is to derive time series of vegetation indices (VIs) which are related to the temporal evolution of FPAR, LAI and GPP or alternatively used to derive phenology metrics that quantify the growing season. The product presented here shows a map of average ‘amplitude’ (i.e. maximum minus minimum) of annual cycles observed in MODIS‐derived NDVI and EVI from 2000 to 2013 for Meso‐ and South America. It is a robust determination of the amplitude of annual cycles of vegetation greenness derived from a Lomb–Scargle spectral analysis of unevenly spaced data. VI time series pre‐processing was used to eliminate measurement outliers, and the outputs of the spectral analysis were screened for statistically significant annual signals. Amplitude maps provide an indication of net ecosystem phenology since the satellite observations integrate the greenness variations across the plant individuals within each pixel. The average amplitude values can be interpreted as indicating the degree to which the leaf life cycles of individual plants and species are synchronized. Areas without statistically significant annual variations in greenness may still consist of individuals that show a well‐defined annual leaf phenology. In such cases, the timing of the phenology events will vary strongly within the year between individuals. Alternatively, such areas may consist mainly of plants with leaf turnover strategies that maintain a constant canopy of leaves of different ages. Comparison with in situ observations confirms our interpretation of the average amplitude measure. VI amplitude interpreted as leaf life cycle synchrony can support model evaluation by informing on the likely leaf turn over rates and seasonal variation in ecosystem leaf age distribution

Evaluating the performance of hydrological models via cross-spectral analysis: case study of the Thames Basin, United Kingdom

Nine distributed hydrological models, forced with common meteorological inputs, simulated naturalised daily discharge from the Thames Basin for 1963-2001. While model-dependent evaporative losses are critical for modelling mean discharge, multiple physical processes at many time scales influence the variability and timing of discharge. Here we advocate the use of cross-spectral analysis to measure how the average amplitude, and independently the average phase, of modelled discharge differ from observed discharge at daily to decadal time scales. Simulation of the spectral properties of the model discharge via numerical manipulation of precipitation confirms that modelled transformation involves runoff generation and routing that amplify the annual cycle, while subsurface storage and routing of runoff between grid boxes introduces most autocorrelation and delays. Too much or too little modelled evaporation affects discharge variability as do the capacity and time constants of modelled stores. Additionally the performance of specific models would improve if four issues were tackled: a) non-sinusoidal annual variations in model discharge (prolonged low baseflow and shortened high baseflow, 3 models), b) excessive attenuation of high frequency variability (3 models), c) excessive short-term variability in winter half years but too little variability in summer half years (2 models) and d) introduction of phase delays at the annual scale only during runoff generation (3 models) or only during routing (1 model). Cross-spectral analysis reveals how re-runs of one model using alternative methods of runoff generation - designed to improve performance at the weekly to monthly time scales - degraded performance at the annual scale. The cross-spectral approach facilitates hydrological model diagnoses and development

WFDE5: Bias-adjusted ERA5 reanalysis data for impact studies

The WFDE5 dataset has been generated using the WATCH Forcing Data (WFD) methodology applied to surface meteorological variables from the ERA5 reanalysis. The WFDEI dataset had previously been generated by applying the WFD methodology to ERA-Interim. The WFDE5 is provided at 0.5 spatial resolution but has higher temporal resolution (hourly) compared to WFDEI (3-hourly). It also has higher spatial variability since it was generated by aggregation of the higher-resolution ERA5 rather than by interpolation of the lower-resolution ERA-Interim data. Evaluation against meteorological observations at 13 globally distributed FLUXNET2015 sites shows that, on average, WFDE5 has lower mean absolute error and higher correlation than WFDEI for all variables. Bias-adjusted monthly precipitation totals of WFDE5 result in more plausible global hydrological water balance components when analysed in an uncalibrated hydrological model (WaterGAP) than with the use of raw ERA5 data for model forcing. The dataset, which can be downloaded from https://doi.org/10.24381/cds.20d54e34 (C3S, 2020b), is distributed by the Copernicus Climate Change Service (C3S) through its Climate Data Store (CDS, C3S, 2020a) and currently spans from the start of January 1979 to the end of 2018. The dataset has been produced using a number of CDS Toolbox applications, whose source code is available with the data - allowing users to regenerate part of the dataset or apply the same approach to other data. Future updates are expected spanning from 1950 to the most recent year. A sample of the complete dataset, which covers the whole of the year 2016, is accessible without registration to the CDS at https://doi.org/10.21957/935p-cj60 (Cucchi et al., 2020). © Author(s) 2020. This work is distributed under the Creative Commons Attribution 4.0 License

Using observed river flow data to improve the hydrological functioning of the JULES land surface model (vn4.3) used for regional coupled modelling in Great Britain (UKC2)

Land surface models (LSMs) represent terrestrial hydrology in weather and climate modelling operational systems and research studies. We aim to improve hydrological performance in the Joint UK Land Environment Simulator (JULES) LSM that is used for distributed hydrological modelling within the new land–atmosphere–ocean coupled prediction system UKC2 (UK regional Coupled environmental prediction system 2). Using river flow observations from gauge stations, we study the capability of JULES to simulate river flow at 1 km2 spatial resolution within 13 catchments in Great Britain that exhibit a variety of climatic and topographic characteristics. Tests designed to identify where the model results are sensitive to the scheme and parameters chosen for runoff production indicate that different catchments require different parameters and even different runoff schemes for optimal results. We introduce a new parameterisation of topographic variation that produces the best daily river flow results (in terms of Nash–Sutcliffe efficiency and mean bias) for all 13 catchments. The new parameterisation introduces a dependency on terrain slope, constraining surface runoff production to wet soil conditions over flatter regions, whereas over steeper regions the model produces surface runoff for every rainfall event regardless of the soil wetness state. This new parameterisation improves the model performance across Great Britain. As an example, in the Thames catchment, which has extensive areas of flat terrain, the Nash–Sutcliffe efficiency exceeds 0.8 using the new parameterisation. We use cross-spectral analysis to evaluate the amplitude and phase of the modelled versus observed river flows over timescales of 2 days to 10 years. This demonstrates that the model performance is modified by changing the parameterisation by different amounts over annual, weekly-to-monthly and multi-day timescales in different catchments, providing insights into model deficiencies on particular timescales, but it reinforces the newly developed parameterisation

Geological controls of discharge variability in the Thames Basin, UK from cross-spectral analyses: observations versus modelling

Geological factors controlling daily- to multi-year discharge variability in 48 sub-catchments spanning 10–1000 km2 in the Thames Basin were investigated using cross-spectral analysis. The analyses represent a ‘transfer function approach’ applied to daily observed streamflow (output) versus catchment-wide precipitation (input) for data spanning 1990–2014. Catchments dominated by high-permeability bedrock have significant attenuation of high-frequency precipitation variability and large delays at all frequencies with streamflow dominated by baseflow (high lag1 autocorrelation and high Base Flow Index, BFI). Catchments dominated by low-permeability rocks have little high-frequency attenuation and small delays and consequently ‘flashy’ behaviour. For all sub-catchments >300 km2 in the Thames Basin, attenuation of the highest frequency precipitation variability caused by mixing of flow from upstream plus groundwater flow (representing ‘older’ variability) with direct surface flow (‘younger’ variability) constitutes real-world moving averaging as indicated by a roll-off in power at the highest frequencies. The success of the JULES land surface model in simulating discharge (i.e. surface and sub-surface runoff routed between grid boxes) is also linked to the underlying geology. Larger catchments (>300 km2) are modelled well because routing between numerous grid boxes leads to moving averaging that is a good analogue for the observations. Modelling was least successful (e.g. lowest Kling-Gupta Efficiency) for small catchments (<300 km2) dominated by high-permeability bedrock - with far too little attenuation of high-frequency precipitation variability and insufficient delays at all frequencies. Experimentally switching the soil saturated hydraulic conductivity to that of the underlying bedrock for grid boxes dominated by aquifers significantly improves modelled discharge variability in small sub-catchments - confirming the importance of bedrock permeability in modelling. For small catchments in data-sparse regions, knowledge of the relative proportions of different hydrogeological units (aquifers, aquitards) potentially could be used to predict and model discharge variability as characterised by BFI and lag1 autocorrelation