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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.

The chapter describes two categories of process-based models, namely, hydrological and socio-hydrological models. It starts with an explanation of the modelling chain from climate drivers via process-based models to identification of hydrological and socio-hydrological drought characteristics. Then, the first category of process-based models is introduced, that is, hydrological models, which are divided in three types: spatially lumped, semi-distributed and spatially distributed models. Spatially distributed models are further subdivided based on whether they simulate lateral groundwater flow (GWfl) or not (nGWfl). An example model of each of these three types, including an application is given, that is, HBV (spatially lumped, semi-distributed), PCR-GLOBWB (spatially distributed model, nGWfl) and SIMGRO (spatially distributed model, GWfl). The second category of process-based models, that is, the socio-hydrological models, is also divided into three types: coupled-component, system-dynamics and agent-based models. The chapter addresses calibration and validation of process-based models and concludes with some guidance on how to select an adequate model, considering also the associated uncertainty.

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.

This paper presents a simple water budget model that can be used to quantify present and to predict future groundwater depletion rates in areas where there is a lack of the comprehensive long-term data needed to develop sophisticated numerical groundwater models. This study applied the water budget model in Wadi Zabid, Yemen, a region where groundwater withdrawals have far exceeded replenishment rates for 50 years, resulting in falling groundwater levels. The findings indicate that the present groundwater use in the wadi is unsustainable, mainly due to the expansion of agricultural lands. The current average groundwater depletion rate was calculated as − 0.93 m/yr, which is in line with the observed average of − 1.11 m/yr (1972–2016). Scenario analysis shows that reducing the groundwater depletion rate by two-thirds of the current rate (from 0.93 to 0.32 m/yr) would require a one-third reduction in agricultural lands (from 435 to 305 km2) in the study region, combined with a one-third increase in surface water inflow from upstream (from 132 to 172 mm/yr, also necessitating a reduction of agriculture in the upstream region). Economic incentives to support alternative livelihoods with lower water requirements, alongside utilization of non-conventional water sources (e.g., exploring the feasibility of seawater desalination) could reduce groundwater depletion. The simple water budget approach proved to be a useful means for this type of analysis in data-scarce regions.

Chapter 1 introduces the reader to the drought hazard and to the scope of the book. How to define drought and the need to distinguish between different types of drought are discussed, underlining the wide range - and often devastating - impacts that drought may cause. An overview of key characteristics of hydrological drought, including low flow and drought deficit characteristics, such as drought duration, is presented and their dependency on the type of hydrological regime highlighted. Drought is considered a natural phenomenon; however, the influence of human interventions and climate change cannot be disregarded. A warming climate has led to a higher awareness of the drought hazard, increased focus on drought research and the need for improved water management and drought preparedness. With this background, a brief history of the advance of drought research and management in Europe over the last decades is provided with reference to major international studies and initiatives. The chapter closes with an overview of the structure of the textbook, including a brief summary of each chapter.

Human activities both aggravate and alleviate streamflow drought. Here we show that aggravation is dominant in contrasting cases around the world analysed with a consistent methodology. Our 28 cases included different combinations of human-water interactions. We found that water abstraction aggravated all drought characteristics, with increases of 20%-305% in total time in drought found across the case studies, and increases in total deficit of up to almost 3000%. Water transfers reduced drought time and deficit by up to 97%. In cases with both abstraction and water transfers into the catchment or augmenting streamflow from groundwater, the water inputs could not compensate for the aggravation of droughts due to abstraction and only shift the effects in space or time. Reservoir releases for downstream water use alleviated droughts in the dry season, but also led to deficits in the wet season by changing flow seasonality. This led to minor changes in average drought duration (-26 to +38%) and moderate changes in average drought deficit (-86 to +369%). Land use showed a smaller impact on streamflow drought, also with both increases and decreases observed (-48 to +98%). Sewage return flows and pipe leakage possibly counteracted the effects of increased imperviousness in urban areas; however, untangling the effects of land use change on streamflow drought is challenging. This synthesis of diverse global cases highlights the complexity of the human influence on streamflow drought and the added value of empirical comparative studies. Results indicate both intended and unintended consequences of water management and infrastructure on downstream society and ecosystems.

Hydrological drought forecasts outperform meteorological ones, which is anticipated coming from catchment memory. Yet, the importance of catchment memory in explaining hydrological drought forecast performance has not been studied. Here, we use the Baseflow Index (BFI) and the groundwater Recession Coefficient (gRC), which through the streamflow, give information on the catchment memory. Performance of streamflow drought forecasts was evaluated using the Brier Score (BS) for rivers across Europe. We found that BS is negatively correlated with BFI, meaning that rivers with high BFI (large memory) yield better drought prediction (low BS). A significant positive correlation between gRC and BS demonstrates that catchments slowly releasing groundwater to streams (low gRC), i.e. large memory, generates higher drought forecast performance. The higher performance of hydrological drought forecasts in catchments with relatively large memory (high BFI and low gRC) implies that Drought Early Warning Systems have more potential to be implemented there and will appear to be more useful.

Streamflow drought forecasting is a key element of contemporary drought early warning systems (DEWS). The term streamflow drought forecasting (not streamflow forecasting), however, has created confusion within the scientific hydrometeorological community as well as in operational weather and water management services. Streamflow drought forecasting requires an additional step, which is the application of a drought identification method to the forecasted streamflow time series. The way streamflow drought is identified is the main reason for this misperception. The purpose of this study, therefore, is to provide a comprehensive overview of the differences between different drought identification approaches to identify droughts in European rivers, including an analysis of both historical drought and implications for forecasting. Streamflow data were obtained from the LISFLOOD hydrological model forced with gridded meteorological observations (known as LISFLOOD-Simulation Forced with Observed, SFO). The same model fed with seasonal meteorological forecasts of the European Centre for Medium-Range Weather Forecasts system 5 (ECMWF SEAS 5) was used to obtain the forecasted streamflow. Streamflow droughts were analyzed using the daily and monthly variable threshold methods (VTD and VTM, respectively), the daily and monthly fixed threshold methods (FTD and FTM, respectively), and the Standardized Streamflow Index (SSI). Our results clearly show that streamflow droughts derived from different approaches deviate from each other in their characteristics, which also vary in different climate regions across Europe. The daily threshold methods (FTD and VTD) identify 25ĝ%-50ĝ% more drought events than the monthly threshold methods (FTM and VTM), and accordingly the average drought duration is longer for the monthly than for the daily threshold methods. The FTD and FTM, in general, identify drought occurrences earlier in the year than the VTD and VTM. In addition, the droughts obtai

Drought is a complex natural phenomenon. The description of the way in which drought changes (moves) in space may help to acquire knowledge on its drivers and processes to improve its monitoring and prediction. This research presents the application of an approach to characterise the dynamics of drought. Tracks, severity, duration, as well as localisation (onset and end position), and rotation of droughts were calculated. Results of calculated droughts were compared with documented information. Data from the Standardized Precipitation Evaporation Index (SPEI) Global Drought Monitor was used to identify droughts in India as an example for the period 1901–2013. Results show regions where droughts with considerable coverage tend to occur. Paths, i.e. consecutive spatial tracks, of six of the most severe reported droughts were analysed. In all of them, areas overlap considerably over time, which suggest that drought remains in the same region for a period of time. Results of this research are being used to build a model to predict the spatial drought tracks, incl. India (https://www.researchgate.net/project/STAND-Spatio-Temporal-ANalysis-of-Drought)., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Water Resources

We thank the authors, Brunella Bonaccorso and Karsten Arnbjerg-Nielsen for their constructive contributions to the discussion about the attribution of changes in drought and flood impacts. We appreciate that they support our opinion, but in particular their additional new ideas on how to better understand changes in impacts. It is great that they challenge us to think a step further on how to foster the collection of long time series of data and how to use these to model and project changes. Here, we elaborate on the possibility to collect time series of data on hazard, exposure, vulnerability and impacts and how these could be used to improve e.g. socio-hydrological models for the development of future risk scenarios.

The spatiotemporal monitoring of droughts is a complex task. In the past decades, drought monitoring has been increasingly developed, while the consideration of its spatio-temporal dynamics is still a challenge. This study proposes a method to build the spatial tracks and paths of drought, which can enhance its monitoring. The steps for the drought tracks calculation are (1) identification of spatial units (areas), (2) centroids localisation, and (3) centroids linkage. The spatio-temporal analysis performed here to extract the areas and centroids builds upon the Contiguous Drought Area (CDA) analysis. The potential of the proposed methodology is illustrated using grid data from the Standardized Precipitation Evaporation Index (SPEI) Global Drought Monitor over India (1901-2013), as an example. The method to calculate the drought tracks allows for identification of drought paths delineated by an onset and an end in space and time. Tracks, severity and duration of the drought are identified, as well as localisation (onset and end position), and rotation. The response of the drought tracking method to different combinations of parameters is also analysed. Further research is in progress to set up a model to predict the drought tracks for particular regions across the world, including India (https://www.researchgate.net/project/STAND-Spatio-Temporal-ANalysis-of-Drought)., Water Resources

Hydrological drought often gets less attention compared to meteorological drought. For water resources managers, information on hydrological drought characteristics is prerequisite for adequate drought planning and management. Therefore, the aim of this study is to analyse hydrological drought characteristics in the pan-European region based on past drought events from 1990 to 2017. The annual average drought duration, deficit volume, onset, termination, and intensity during drought years were calculated using daily runoff and groundwater data. All data were simulated with the LISFLOOD hydrological model (resolution 5×5km) fed with gridded time series of observed weather data. Results based on runoff and groundwater data show that regions in Northeast to Southeast Europe, which stretched out from Poland to Bulgaria, were identified as profound regions to severe hydrological drought hazards. The most severe droughts during our study period were observed in 1992 to 1997, where on average Europe experienced drought events, which lasted up to 4 months. Long average drought durations up to 4 and 8 months in runoff and groundwater occurred in a few parts of the European regions (around 10% area). Longer drought durations and a lower number of drought events were found in groundwater drought than in runoff, which proved that slow responding variables (groundwater) are better in showing extreme drought compared to fast responding variables (runoff). Based on our results, the water managers can better prepare for upcoming drought and foster drought adaptation actions.

Compound and cascading natural hazards usually cause more severe impacts than any of the single hazard events alone. Despite the significant impacts of compound hazards, many studies have only focused on single hazards. The aim of this paper is to investigate spatio-temporal patterns of compound and cascading hazards using historical data for dry hazards, namely heatwaves, droughts, and fires across Europe. We streamlined a simple methodology to explore the occurrence of such events on a daily basis. Droughts in soil moisture were analyzed using time series of a threshold-based index, obtained from the LISFLOOD hydrological model forced with observations. Heatwave and fire events were analyzed using the ERA5-based temperature and Fire Weather Index datasets. The data used in this study relates to the summer seasons from 1990 to 2018. Our results show that joint dry hazard occurrences were identified in west, central, and east Europe, and with a lower frequency in southern Europe and eastern Scandinavia. Drought plays a substantial role in the occurrence of the compound and cascading events of dry hazards, especially in southern Europe as it drives duration of cascading events. Moreover, drought is the most frequent hazard-precursor in cascading events, followed by compound drought-fire events. Changing the definition of a cascading dry hazard by increasing the number of days without a hazard from 1 to 21 within the event (inter-event criterion), lowers as expected, the maximum number of cascading events from 94 to 42, and extends the maximum average duration of cascading events from 38 to 86 days. We had to use proxy observed data to determine the three selected dry hazards because long time series of reported dry hazards do not exist. A complete and specific database with reported hazards is a prerequisite to obtain a more comprehensive insight into compound and cascading dry hazards.

Drought early warning systems (DEWS) have been developed in several countries in response to high socioeconomic losses caused by droughts. In Europe, the European Drought Observatory (EDO) monitors the ongoing drought and forecasts soil moisture anomalies up to 7 days ahead and meteorological drought up to 3 months ahead. However, end users managing water resources often require hydrological drought warning several months in advance. To answer this challenge, a seasonal pan-European DEWS has been developed and has been running in a preoperational mode since mid-2018 under the EU-funded Enhancing Emergency Management and Response to Extreme Weather and Climate Events (ANYWHERE) project. The ANYWHERE DEWS (AD-EWS) is different than other operational DEWS in the sense that the AD-EWS provides a wide range of seasonal hydrometeorological drought forecasting products in addition to meteorological drought, that is, a broad suite of drought indices that covers all water cycle components (drought in precipitation, soil moisture, runoff, discharge, and groundwater). The ability of the AD-EWS to provide seasonal drought predictions in high spatial resolution (5 km × 5 km) and its diverse products mark the AD-EWS as a preoperational drought forecasting system that can serve a broad range of different users' needs in Europe. This paper introduces the AD-EWS and shows some examples of different drought forecasting products, the drought forecast score, and some examples of a user-driven assessment of forecast trust levels.

Forecasting of drought impacts is still lacking in drought early-warning systems (DEWSs), which presently do not go beyond hazard forecasting. Therefore, we developed drought impact functions using machine learning approaches (logistic regression and random forest) to predict drought impacts with lead times up to 7 months ahead. The observed and forecasted hydrometeorological drought hazards – such as the standardized precipitation index (SPI), standardized precipitation evaporation index (SPEI), and standardized runoff index (SRI) – were obtained from the The EU-funded Enhancing Emergency Management and Response to Extreme Weather and Climate Events (ANYWHERE) DEWS. Reported drought impact data, taken from the European Drought Impact Report Inventory (EDII), were used to develop and validate drought impact functions. The skill of the drought impact functions in forecasting drought impacts was evaluated using the Brier skill score and relative operating characteristic metrics for five cases representing different spatial aggregation and lumping of impacted sectors. Results show that hydrological drought hazard represented by SRI has higher skill than meteorological drought represented by SPI and SPEI. For German regions, impact functions developed using random forests indicate a higher discriminative ability to forecast drought impacts than logistic regression. Moreover, skill is higher for cases with higher spatial resolution and less lumped impacted sectors (cases 4 and 5), with considerable skill up to 3–4 months ahead. The forecasting skill of drought impacts using machine learning greatly depends on the availability of impact data. This study demonstrates that the drought impact functions could not be developed for certain regions and impacted sectors, owing to the lack of reported impacts.

One of the most effective strategies to reduce the impacts of drought is by issuing a timely and targeted warning from month to seasons ahead to end users. Yet to accurately forecast the drought hazard on a sub-seasonal to seasonal time scale remains a challenge, and usually, meteorological drought is forecasted instead of hydrological drought, although the latter is more relevant for several impacted sectors. Therefore, we evaluate the hydro-meteorological drought forecast skill for the pan-European region using categorical drought classification method. The results show that the hydrological drought forecasts outperform the meteorological drought forecasts. Hydrological drought forecasts even show predictive power (area with perfect prediction >50%) beyond 2 months ahead. Our study also concludes that dynamical forecasts, derived from seasonal climate forecasts, have higher predictability than ensemble streamflow predictions. The results suggest that further development of seasonal hydrological drought forecasting systems are beneficial, particularly important in the context of global warming, where drought hazard will become more frequent and severe in multiple regions in the world.