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Drought is a ubiquitous and reoccurring hazard that has wide ranging impacts on society, agriculture and the environment. Drought indices are vital for characterizing the nature and severity of drought hazards, and there have been extensive efforts to identify the most suitable drought indices for drought monitoring and risk assessments. However, to date, little effort has been made to explore which index(s) best represents drought impacts for various sectors in China. This is a critical knowledge gap, as impacts provide important ‘ground truth’ information. They can be used to demonstrate whether drought indices (used for monitoring or risk assessment) are relevant for identifying impacts, thus highlighting if an area is vulnerable to drought of a given severity. The aim of this study is to explore the link between drought indices and drought impacts, using Liaoning province (northeast China) as a case study due to its history of drought occurrence. To achieve this we use independent, but complementary, methods (correlation and random forest analysis). Using multiple drought indices – Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Soil Moisture (SoilM) and the Normalized Difference Vegetation Index (NDVI) – and drought impact data (on crop yield, livestock, rural people and the economy) correlation and random forest analysis were used to identify which indices link best to the recorded drought impacts for cities in Liaoning. The results show that the relationship varies between different categories of drought impacts and between cities. SPEI with a 6-month accumulation (SPEI6) had a strong correlation with all categories of drought impacts, while SPI12 had a weak correlation with drought impacts. Of the impact datasets, drought suffering area and drought impact area had a slightly strong relationship with all drought indices in Liaoning province, while population and number of livestock with difficulty in accessing drinking water had weak correlations with the indices. Based on the linkage, drought vulnerability was analyzed using various vulnerability factors. Crop cultivated area was positively correlated to the drought vulnerability for five out of the eight categories of drought impacts, while the total population had a strong negative relationship with drought vulnerability for half the drought impact categories. This study can support drought planning efforts in the region, and provides a methodology for application for other regions of China (and other countries) in the future, as well as providing context for the indices used in drought monitoring applications, so enabling improved preparedness for drought impacts.

Hydrological models can provide estimates of streamflow pre- and post-observations, which enable greater understanding of past hydrological behaviour, and potential futures. In this paper, a new multi-objective calibration method was derived and tested for 303 catchments in the UK, and the calibrations were used to reconstruct river flows back to 1891, in order to provide a much longer view of past hydrological variability, given the brevity of most UK river flow records which began post-1960. A Latin hypercube sample of 500 000 parameterisations for the GR4J model for each catchment were evaluated against six evaluation metrics covering all aspects of the flow regime from high, median, and low flows. The results of the top ranking model parameterisation (LHS1), and also the top 500 (LHS500), for each catchment were used to provide a deterministic result whilst also accounting for parameter uncertainty. The calibrations are generally good at capturing observed flows, with some exceptions in heavily groundwater-dominated catchments, and snowmelt and artificially influenced catchments across the country. Reconstructed flows were appraised over 30-year moving windows and were shown to provide good simulations of flow in the early parts of the record, in cases where observations were available. To consider the utility of the reconstructions for drought simulation, flow data for the 1975–1976 drought event were explored in detail in nine case study catchments. The model's performance in reproducing the drought events was found to vary by catchment, as did the level of uncertainty in the LHS500. The Standardised Streamflow Index (SSI) was used to assess the model simulations' ability to simulate extreme events. The peaks and troughs of the SSI time series were well represented despite slight over- or underestimations of past drought event magnitudes, while the accumulated deficits of the drought events extracted from the SSI time series verified that the model simulations were overall very good at simulating drought events. This paper provides three key contributions: (1) a robust multi-objective model calibration framework for calibrating catchment models for use in both general and extreme hydrology; (2) model calibrations for the 303 UK catchments that could be used in further research, and operational applications such as hydrological forecasting; and (3) ∼ 125 years of spatially and temporally consistent reconstructed flow data that will allow comprehensive quantitative assessments of past UK drought events, as well as long-term analyses of hydrological variability that have not been previously possible, thus enabling water resource managers to better plan for extreme events and build more resilient systems for the future.

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.

In 2015 large parts of Europe were affected by drought. In this paper, we analyze the hydrological footprint (dynamic development over space and time) of the drought of 2015 in terms of both severity (magnitude) and spatial extent and compare it to the extreme drought of 2003. Analyses are based on a range of low flow and hydrological drought indices derived for about 800 streamflow records across Europe, collected in a community effort based on a common protocol. We compare the hydrological footprints of both events with the meteorological footprints, in order to learn from similarities and differences of both perspectives and to draw conclusions for drought management. The region affected by hydrological drought in 2015 differed somewhat from the drought of 2003, with its center located more towards eastern Europe. In terms of low flow magnitude, a region surrounding the Czech Republic was the most affected, with summer low flows that exhibited return intervals of 100 years and more. In terms of deficit volumes, the geographical center of the event was in southern Germany, where the drought lasted a particularly long time. A detailed spatial and temporal assessment of the 2015 event showed that the particular behavior in these regions was partly a result of diverging wetness preconditions in the studied catchments. Extreme droughts emerged where preconditions were particularly dry. In regions with wet preconditions, low flow events developed later and tended to be less severe. For both the 2003 and 2015 events, the onset of the hydrological drought was well correlated with the lowest flow recorded during the event (low flow magnitude), pointing towards a potential for early warning of the severity of streamflow drought. Time series of monthly drought indices (both streamflow- and climate-based indices) showed that meteorological and hydrological events developed differently in space and time, both in terms of extent and severity (magnitude). These results emphasize that drought is a hazard which leaves different footprints on the various components of the water cycle at different spatial and temporal scales. The difference in the dynamic development of meteorological and hydrological drought also implies that impacts on various water-use sectors and river ecology cannot be informed by climate indices alone. Thus, an assessment of drought impacts on water resources requires hydrological data in addition to drought indices based solely on climate data. The transboundary scale of the event also suggests that additional efforts need to be undertaken to make timely pan-European hydrological assessments more operational in the future.

The English Lowlands is a relatively dry, densely populated region in the southeast of the UK in which water is used very intensively. Consequently, parts of the region are water-stressed and face growing water resource pressures. The region is heavily dependent on groundwater and particularly vulnerable to long, multi-annual droughts, primarily associated with dry winters. Despite this vulnerability, the atmospheric drivers of multi-annual droughts in the region are poorly understood, an obstacle to developing appropriate drought management strategies, including monitoring and early warning systems. To advance our understanding, we assess known key climate drivers in the winter half-year (October–March), and their likely relationships with multi-annual droughts in the region. We characterise historic multi-annual drought episodes back to 1910 for the English Lowlands using various meteorological and hydrological datasets. Multi-annual droughts are identified using a gridded precipitation series for the entire region, and refined using the Standardized Precipitation Index (SPI) and a Standardized Groundwater level Index (SGI) applied to regional-scale river flow and groundwater time series. We explore linkages between a range of potential climatic driving factors and precipitation, river flow and groundwater level indicators in the English Lowlands for the winter half-year. The drivers or forcings include El Niño–Southern Oscillation (ENSO), the North Atlantic Tripole Sea Surface Temperature (SST) pattern, the Quasi–Biennial Oscillation (QBO), solar and volcanic forcing and the Atlantic Multi-decadal Oscillation (AMO). As expected, no single driver convincingly explains the occurrence of any multi-annual drought in the historical record. However, we demonstrate, for the first time, an association between La Niña episodes and winter rainfall deficits in some major multi-annual drought episodes in the English Lowlands. We also show significant (albeit relatively weak) links between ENSO and drought indicators applied to river flow and groundwater levels. We also show that some of the other drivers listed above are likely to influence English Lowlands rainfall. We conclude by signposting a direction for this future research effort.

An overall appraisal of runoff changes at the European scale has been hindered by "white space" on maps of observed trends due to a paucity of readily-available streamflow data. This study tested whether this white space can be filled using estimates of trends derived from model simulations of European runoff. The simulations stem from an ensemble of eight global hydrological models that were forced with the same climate input for the period 1963–2000. The derived trends were validated for 293 grid cells across the European domain with observation-based trend estimates. The ensemble mean overall provided the best representation of trends in the observations. Maps of trends in annual runoff based on the ensemble mean demonstrated a pronounced continental dipole pattern of positive trends in western and northern Europe and negative trends in southern and parts of eastern Europe, which has not previously been demonstrated and discussed in comparable detail. Overall, positive trends in annual streamflow appear to reflect the marked wetting trends of the winter months, whereas negative annual trends result primarily from a widespread decrease in streamflow in spring and summer months, consistent with a decrease in summer low flow in large parts of Europe. High flow appears to have increased in rain-dominated hydrological regimes, whereas an inconsistent or decreasing signal was found in snow-dominated regimes. The different models agreed on the predominant continental-scale pattern of trends, but in some areas disagreed on the magnitude and even the direction of trends, particularly in transition zones between regions with increasing and decreasing runoff trends, in complex terrain with a high spatial variability, and in snow-dominated regimes. Model estimates appeared most reliable in reproducing observed trends in annual runoff, winter runoff, and 7-day high flow. Modelled trends in runoff during the summer months, spring (for snow influenced regions) and autumn, and trends in summer low flow were more variable – both among models and in the spatial patterns of agreement between models and the observations. The use of models to display changes in these hydrological characteristics should therefore be viewed with caution due to higher uncertainty.