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Urbanisation is an important driver of changes in streamflow. These changes are not uniform across catchments due to the diverse nature of water sources, storage, and pathways in urban river systems. While land cover data are typically used in urban hydrology analyses, other characteristics of urban systems (such as water management practices) are poorly quantified which means that urbanisation impacts on streamflow are often difficult to detect and quantify. Here, we assess urban impacts on streamflow dynamics for 711 catchments across England and Wales. We use the CAMELS-GB dataset, which is a large-sample hydrology dataset containing hydro-meteorological timeseries and catchment attributes characterising climate, geology, water management practices and land cover. We quantify urban impacts on a wide range of streamflow dynamics (flow magnitudes, variability, frequency, and duration) using random forest models. We demonstrate that wastewater discharges from sewage treatment plants and urban land cover dominate urban hydrology signals across England and Wales. Wastewater discharges increase low flows and reduce flashiness in urban catchments. In contrast, urban land cover increases flashiness and frequency of medium and high flow events. We highlight the need to move beyond land cover metrics and include other features of urban river systems in hydrological analyses to quantify current and future drivers of urban streamflow.

Human interventions influence drought propagation and low flow in a number of ways. Abstraction decreases low flow and makes hydrological drought events more severe; water transfers do the opposite. Reservoirs change water availability in time, and, dependent on the reservoir management, low flow can be increased and hydrological drought shifted in time. Land surface changes affect evapotranspiration, infiltration and surface runoff processes, influencing the timing and severity of hydrological drought. Qualitative methods (e.g., interviews, surveys) and socio-hydrological models can give valuable insights into the complexity of human-water interactions. Socio-hydrological models can also be used to explore feedbacks of water management scenarios. To quantify of the effect of natural and human drivers several approaches using observations, modelling or a combination of both can be used. Here, we demonstrate the use of these approaches to selected case studies around the world and show that the human influence on low flow and hydrological drought can be both aggravating and alleviating.

In this chapter, you will find an overview of the most used hydrological drought characteristics and their derivation. This includes single low flow indices, such as percentiles from the flow duration curve; drought deficit characteristics derived by the threshold level method, such as duration and deficit volume, standardised indices, such as the Standardised Groundwater Level Index, multivariate indices based on modelling or a combination of ‘stand alone’ indices, and spatial drought indices. We explain methods of calculation in terms of both time series and single indices and give examples of how to derive different indices through worked examples. The methods described are applicable for both observed and simulated time series of hydrological variables representing a flux, such as river flow, or a state variable, such as groundwater levels. We also give general recommendations for the choice of index based on the problem under study, data availability and climatic and regional characteristics. Finally, we describe the relationship between different drought indices.

Hydrological data are the foundation of hydrological research, as well as effective water management. Hydrological data are therefore of pivotal importance to any drought or low flow study. While there have been significant advances in hydrological data acquisition, analysis and dissemination in recent decades, it is still crucially important to ensure data are of suitable quality to support analysis and decision-making. In this chapter, key concepts in hydrological data are first reviewed, and then main types of hydrological data (river flow and groundwater level) are explored, giving due attention to how they are derived, including challenges and limitations. Key supporting datasets are also considered, such as hydrometeorological datasets (e.g., precipitation, evaporation, soil moisture) and spatial datasets available at different scales (local, regional and global) that provide important context for hydrological observations. Example datasets containing flow data are provided that cover different scales (international, regional and local). Local datasets also include groundwater and meteorological data.

Groundwater-dominated catchments are often critical for nationally important water resources. Many conceptual rainfall–runoff models tend to degrade in their model performance in groundwater-dominated catchments, as they are rarely designed to simulate spatial groundwater behaviours or interactions with surface waters. Intercatchment groundwater flow is one such neglected variable. Efforts have been made to incorporate this process into existing models, but there is a need for improving our perceptual models of groundwater–surface water interactions prior to any model modifications. In this study, national meteorological, hydrological, hydrogeological, geological and artificial influence (characterising abstractions and return flows) datasets are used to infer a perceptual model of intercatchment groundwater flow (IGF) and how it varies across the river Thames, United Kingdom (UK). We characterise the water balance, presence of gaining/losing river reaches and intra-annual dynamics in 80 subcatchments of the river Thames, taking advantage of its wealth of data, densely gauged river network and geological variability. We show the prevalence of non-conservative river reaches across the study area, with heterogeneity both between, and within, geological units, giving rise to a complex distribution of recharge and discharge points along the river network. We infer where non-conservative reaches can be attributed to IGF and where other processes (e.g. surface water abstractions) are the likely cause. Through analysis of recorded reach length water balance data and hydrogeological perceptualisation, we conclude that outcrops of carbonate fractured aquifers (Chalk and Jurassic limestone) show evidence of IGF both from headwater to downstream reaches, and out-of-catchment via spring lines. We found variability across the study area, with more seasonality and variability in river catchments on Jurassic limestone outcrops compared to Chalk and Lower Greensand outcrops. Our re

Drought forecasting and early warning systems for water resource extremes are increasingly important tools in water resource management in Europe where increased population density and climate change are expected to place greater pressures on water supply. In this context, the North Atlantic Oscillation (NAO) is often used to indicate future water resource behaviours (including droughts) over Europe, given its dominant control on winter rainfall totals in the North Atlantic region. Recent hydroclimate research has focused on the role of multiannual periodicities in the NAO in driving low frequency behaviours in some water resources, suggesting that notable improvements to lead-times in forecasting may be possible by incorporating these multiannual relationships. However, the importance of multiannual NAO periodicities for driving water resource behaviour, and the feasibility of this relationship for indicating future droughts, has yet to be assessed in the context of known non-stationarities that are internal to the NAO and its influence on European meteorological processes. Here we quantify the time–frequency relationship between the NAO and a large dataset of water resources records to identify key non-stationarities that have dominated multiannual behaviour of water resource extremes over recent decades. The most dominant of these is a 7.5-year periodicity in water resource extremes since approximately 1970 but which has been diminishing since 2005. Furthermore, we show that the non-stationary relationship between the NAO and European rainfall is clearly expressed at multiannual periodicities in the water resource records assessed. These multiannual behaviours are found to have modulated historical water resource anomalies to an extent that is comparable to the projected effects of a worst-case climate change scenario. Furthermore, there is limited systematic understanding in existing atmospheric research for non-stationarities in these periodic behaviours which

Fractal analysis of a time series provides information on how the series varies across all (possible) temporal scales with respect to a given statistical measure. Dynamic hydrological models are typically optimized/calibrated using performance criteria defined in the time domain; however, the performance of models in simulating the fluctuation structure of a time series is seldom investigated. We use a multi-objective pattern search algorithm to calibrate a combined 15-minute resolution recharge–groundwater flow model. The non-dominated simulations of the model are then analysed in the fractal domain using robust detrended fluctuation analysis. The results show that some non-dominated simulations can be eliminated based on poor performance in the fractal domain, hence ensuring that the fluctuation structure of the optimized simulations is captured; this was named fractal-domain-refinement. Furthermore, some recharge parameters are sensitive to fractal-domain-refinement. This gives insights into which parameters are sensitive to the fractal behaviour of the simulated variable.

Water resource forecasting generally centres on understanding hydrological variability over coming months or years, so that water managers can prepare for extremes such as droughts or floods (Chang & Guo, 2020; Hao et al., 2018). Some forecasting systems seek to project further into the future to allow long‐term planning of infrastructure and resilience to extremes and climate change (Svensson et al., 2015). These systems can rely directly or indirectly on outputs from Global Climate Models (GCMs; such as gridded reanalysis datasets) to forecast hydrological conditions (Bhatt & Mall, 2015; Ionita & Nagavciuc, 2020). In the North Atlantic region, in particular Western Europe, the North Atlantic Oscillation (NAO) is used as an indicator for hydrometeorological conditions given its leading control on winter rainfall totals (Hurrell & Deser, 2010; Scaife et al., 2008, 2014). A dipole of pressure anomalies over the North Atlantic, the NAO's positive phase (greater than average pressure gradient; NAO+) results in wetter conditions in northwest Europe with dryer conditions in southwest Europe (Rust et al., 2018; Trigo et al., 2004). Its negative phase (weaker than average pressure gradient; NAO−) results in the inverse effect on rainfall (Folland et al., 2015; and as shown by the correlation coefficients in Figure 1). Given this relationship, and, considering the role of winter rainfall variability in groundwater drought development (e.g., reduced winter recharge) and generation of late winter/early spring floods, the NAO offers a potential explanatory variable when understanding the behaviour of some hydrological extremes. image