Over recent decades, the global population has been rapidly increasing and human activities have altered terrestrial water fluxes to an unprecedented extent. The phenomenal growth of the human footprint has significantly modified hydrological processes in various ways (e.g. irrigation, artificial dams, and water diversion) and at various scales (from a watershed to the globe). During the early 1990s, awareness of the potential for increased water scarcity led to the first detailed global water resource assessments. Shortly thereafter, in order to analyse the human perturbation on terrestrial water resources, the first generation of largescale hydrological models (LHMs) was produced. However, at this early stage few models considered the interaction between terrestrial water fluxes and human activities, including water use and reservoir regulation, and even fewer models distinguished water use from surface water and groundwater resources. Since the early 2000s, a growing number of LHMs have incorporated human impacts on the hydrological cycle, yet the representation of human activities in hydrological models remains challenging. In this paper we provide a synthesis of progress in the development and application of human impact modelling in LHMs. We highlight a number of key challenges and discuss possible improvements in order to better represent the human-water interface in hydrological models.

Large-scale hydrological models are used to determine drought on a global scale. However, it is important to know how well these large-scale models can reproduce major drought events in the past before projections can be made. This study presents a comparison between a multi-model ensemble and reported drought events in the literature to assess the performance of large-scale models. Major drought events in the selected period (1963-2000) were reproduced by the model ensemble median, although the duration and spatial extent differed substantially from reported events. The major drought events are caused by precipitation deficits linked to oscillations in climatic patterns, such as ENSO. This implies that major drought events were simulated if these were included in the forcing data. Spatial extent and duration of simulated drought events differed from extent and duration of reported ones due to a fast runoff response in some models.

Drought severity and related socio-economic impacts are expected to increase due to climate change. To better adapt to these impacts, more knowledge on changes in future hydrological drought characteristics (e.g. frequency, duration) is needed rather than only knowledge on changes in meteorological or soil moisture drought characteristics. In this study, effects of climate change on droughts in several river basins across the globe were investigated. Downscaled and bias-corrected data from three General Circulation Models (GCMs) for the A2 emission scenario were used as forcing for large-scale models. Results from five large-scale hydrological models (GHMs) run within the EU-WATCH project were used to identify low flows and hydrological drought characteristics in the control period (1971-2000) and the future period (2071-2100). Low flows were defined by the monthly 20th percentile from discharge (Q20). The variable threshold level method was applied to determine hydrological drought characteristics. The climatology of normalized Q20 from model results for the control period was compared with the climatology of normalized Q20 from observed discharge of the Global Runoff Data Centre. An observation-constrained selection of model combinations (GHM and GCM) was made based on this comparison. Prior to the assessment of future change, the selected model combinations were evaluated against observations in the period 2001-2010 for a number of river basins. The majority of the combinations (82%) that performed sufficiently in the control period, also performed sufficiently in the period 2001-2010. With the selected model combinations, future changes in drought for each river basin were identified. In cold climates, model combinations projected a regime shift and increase in low flows between the control period and future period. Arid climates were found to become even drier in the future by all model combinations. Agreement between the combinations on future low flows was low in humid climates. Changes in hydrological drought characteristics relative to the control period did not correspond to changes in low flows in all river basins. In most basins (around 65%), drought duration and deficit were projected to increase by the majority of the selected model combinations, while a decrease in low flows was projected in less basins (around 51%). Even if low discharge (monthly Q20) was not projected to decrease for each month, droughts became more severe, for example in some basins in cold climates. This is partly caused by the use of the threshold of the control period to determine drought events in the future, which led to unintended droughts in terms of expected impacts. It is important to consider both low discharge and hydrological drought characteristics to anticipate on changes in droughts for implementation of correct adaptation measures to safeguard future water resources.

During the past decades large-scale models have been developed to simulate global and continental terrestrial water cycles. It is an open question whether thesemodels are suitable to capture hydrological drought, in terms of runoff, on a global scale. Amultimodel ensemble analysis was carried out to evaluate if 10 such large-scalemodels agree on major drought events during the second half of the twentieth century. Time series of monthly precipitation, monthly total runofffrom 10 global hydrologicalmodels, and their ensemblemedian have been used to identify drought. Temporal development of area in drought for various regions across the globe was investigated. Model spread was largest in regions with low runoffand smallest in regions with high runoff. In vast regions, correlation between runoffdrought derived from the models and meteorological drought was found to be low. This indicated that models add information to the signal derived from precipitation and that runoffdrought cannot directly be determined from precipitation data alone in global drought analyses with a constant aggregation period. However, duration and spatial extent of major drought events differed between models. Some models showed a fast runoffresponse to rainfall, which led to deviations from reported drought events in slowly responding hydrological systems. By using an ensemble of models, this fast runoffresponse was partly overcome and delay in drought propagating from meteorological drought to drought in runoffwas included. Finally, an ensemble of models also allows for consideration of uncertainty associated with individual model structures.

The identification of hydrological drought at global scale has received considerable attention during the last decade. However, climate-induced variation in runoff across the world makes such analyses rather complicated. This especially holds for the drier regions of the world (both cold and warm), where, for a considerable period of time, zero runoff can be observed. In the current paper, we present a method that enables to identify drought at global scale across climate regimes in a consistent manner. The method combines the characteristics of the classical variable threshold level method that is best applicable in regions with non-zero runoff most of the time, and the consecutive dry days (period) method that is better suited for areas where zero runoff occurs. The newly presented method allows a drought in periods with runoff to continue in the following period without runoff. The method is demonstrated by identifying droughts from discharge observations of four rivers situated within different climate regimes, as well as from simulated runoff data at global scale obtained from an ensemble of five different land surface models. The identified drought events obtained by the new approach are compared to those resulting from application of the variable threshold level method or the consecutive dry period method separately. Results show that, in general, for drier regions, the threshold level method overestimates drought duration, because zero runoff periods are included in a drought, according to the definition used within this method. The consecutive dry period method underestimates drought occurrence, since it cannot identify droughts for periods with runoff. The developed method especially shows its relevance in transitional areas, because, in wetter regions, results are identical to the classical threshold level method. By combining both methods, the new method is able to identify single drought events that occur during positive and zero runoff periods, leading to a more realistic global drought characterization, especially within drier environments.