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Climate change has increased the possibility of more severe and prolonged droughts worldwide, which requires innovative methods to predict their impacts on different sectors such as agriculture. Crop growth models calculate yield and variables related to plant development and are used for crop yield estimation, a useful variable for monitoring drought impacts. Although used for prediction, these crop models are not explicit forecasting models; they are limited to the physical assumptions reflected in their conceptual model. In addition, the input data availability, the spatial and temporal aggregation, and different sources of uncertainty make the crop yield prediction challenging. Given these limitations, machine learning (ML) models are often utilised following a multivariable forecasting approach, but their use with the spatial characteristics of droughts as input data is limited. This research explored the spatial extent of drought as input data for building an approach for predicting seasonal crop yield. This ML approach is made up of two components. The first includes polynomial regression (PR) models, and the second considers artificial neural network (ANN) models. This approach aimed to evaluate both types of ML models (PR and ANN) and integrate them into one operational tool. The logic is as follows: ANN models determine the most accurate predictions, but in practice, issues regarding data retrieval and processing can make the use of equations, i.e. PR, preferable. The proposed approach provides these PR equations with early and preliminary input to perform such calculations. The estimates can be further improved when the ANN models are run with the final input data. The results indicated that the empirical equations (PR) produced good predictions when using drought area as the input. ANN provides better estimates, in general. Research results show that the spatiotemporal changes of drought area and its temporal aggregation provide an important pre-processing alternative to implement ML models for drought impact prediction.

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.

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.

Crop yield is one of the variables used to assess the impact of droughts on agriculture. Crop growth models calculate yield and variables related to plant development and become more suitable for crop yield estimation. However, these models are limited in that specific data are needed for computation. Given this limitation, machine learning (ML) models are often widely utilised instead, but their use with the spatial characteristics of droughts as input data is limited. This research explored the spatial extent of drought (area) as input data for building an approach to predict seasonal crop yield. This ML approach is made up of two components. The first includes polynomial regression (PR) models, and the second considers artificial neural network (ANN) models. In this approach, the purpose was to evaluate both types of ML models (PR and ANN) and integrate them into one operational tool. The logic is as follows: ANN models determine the most accurate predictions, but in practice, issues regarding data retrieval and processing can make the use of equations, i.e. PR, preferable. The proposed approach provides these PR equations to perform such calculations with early and preliminary input. The estimates can be further improved when the ANN models are run with the final input data. The results indicated that the empirical equations (PR) produced good predictions when using drought area as the input. ANN provides better estimates, in general. This research will improve drought monitoring systems for assessing drought effects. Since it is currently possible to calculate drought areas within these systems, the direct application of the prediction of drought effects is possible to integrate by following approaches such as the one presented or similar.

In its three-dimensional (3-D) characterization, drought is approached as an event whose spatial extent changes over time. Each drought event has an onset and end time, a location, a magnitude, and...

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.

For the development of sustainable, efficient risk management strategies for the hydrological extremes of droughts and floods, it is essential to understand the temporal changes of impacts, and their respective causes and interactions. In particular, little is known about changes in vulnerability and their influence on drought and flood impacts. We present a fictitious dialogue between two experts, one in droughts and the other in floods, showing that the main obstacles to scientific advancement in this area are both a lack of data and a lack of commonly accepted approaches. The drought and flood experts “discuss” available data and methods and we suggest a complementary approach. This approach consists of collecting a large number of single or multiple paired-event case studies from catchments around the world, undertaking detailed analyses of changes in impacts and drivers, and carrying out a comparative analysis. The advantages of this approach are that it allows detailed context- and location-specific assessments based on the paired-event analyses, and reveals general, transferable conclusions based on the comparative analysis of various case studies. Additionally, it is quite flexible in terms of data and can accommodate differences between floods and droughts.

Streamflow drought forecasting is a key element of contemporary Drought Early Warning Systems (DEWS). The term streamflow drought forecasting, rather than streamflow forecasting, however, has created confusion within the scientific hydro-meteorological 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, how streamflow drought is defined, is the main reason for this misperception. The purpose of this study, therefore, is to provide a comprehensive overview of the application of different drought identification approaches to forecast streamflow drought, incl. its characteristics, such as drought occurrence, timing, duration, and deficit volume, across the pan-European river network and for the Rhine River in more detail. In this study, the implications of different approaches for forecasting streamflow drought are elaborated using the extreme 2003 drought in Europe, as an example. The forecasted 25 ensemble streamflow data with 7-month lead time (LT) were obtained from the LISFLOOD hydrological model fed with seasonal meteorological forecasts from the European Centre for Medium-range Weather Forecasts system 5 (ECMWF SEAS 5). Streamflow droughts were analyzed using the daily and monthly Variable Threshold methods (VTD and VTM), daily and monthly Fixed Threshold methods (FTD and FTM), and the Standardized Streamflow Index with 1-month accumulation period (SSI-1). Our results clearly show that streamflow drought characteristics derived with different approaches deviate, which are partly associated with different climate regions across Europe. Using the forecasts initiated in July 2003 for LT=7-month, first, the daily drought approaches forecast more drought events than the monthly approaches. Second, the VT droughts (VTD and VTM), incl. SSI-1 forecast a lower number of drought occurrences than the FT droughts (FTD and FTM), which highlights the importance of taking seasonality into account. Overall, the FT approaches predict a longer drought duration, earlier drought timing, and higher drought deficit volume in many European rivers than the VT approaches. The characteristics of SSI-1 drought, in general, are close to what is being identified by the VTM approach. A detailed analysis of the drought forecasts for the Rhine River indicates that the number of drought events derived from the median of ensemble members can be predicted relatively well, but with lower skill for other drought characteristics. The use of monthly-aggregated forecasted flow data (e.g. VTM, FTM, and SSI) seems to be the best practice for seasonal drought forecasts because it will alleviate the drought forecast skill. The monthly drought threshold approaches, however, will forecast higher drought duration and deficit volume than using daily datasets. The choice of the drought identification method when forecasting streamflow drought, ultimately rests with the end-users and we need to realize that there is no one drought identification approach that fits all needs.

Several approaches to identify hydrological drought exist, which result in differences in drought frequency, timing, duration, and deficit volume (drought characteristics) using the same hydrometeorological data as input. This has created confusion within the hydro-meteorological community, as well as in operational water management services on the difference in drought characteristics obtained with the different approaches. The aim of this study, therefore, is to provide a comprehensive overview of the differences of hydrological drought, i.e. streamflow drought, using different identification approaches for the pan-European river network (>10,000 river grid cells). Time series of daily streamflow data were obtained from the LISFLOOD hydrological model forced with gridded meteorological observations from 1990 to 2018. Streamflow droughts were detected using the daily and monthly Variable Threshold methods (VTD and VTM), daily and monthly Fixed Threshold methods (FTD and FTM), and the Standardized Streamflow Index with 1-month accumulation period (SSI-1). For the threshold methods the Q80 (flow that is equaled or exceeded 80 percent of the time) is applied, whereas for the SSI a threshold of about -1 is used. We applied a centered 30-day moving average (30DMA) smoothing technique to the daily flow data to reduce the number of minor droughts. This is the first study that compares all these drought identification approaches in such a systematic way at this large scale. Our results (pan-European maps, tables) clearly show that characteristics of streamflow droughts derived with different approaches deviate, partly associated with different climate regions across Europe. The daily threshold methods (VTD and FTD) identify twice as much drought events than the monthly threshold methods (VTM and FTM) due to the daily resolution and minor droughts, even with smoothing. Average duration of FT droughts is longer than VT droughts. In addition, FT droughts have higher drought deficit volumes than VT droughts (~ 30-60%, dependent on climate region), whereas using monthly data (VTM and FTM) result in higher deficits (~10-60%) than daily data (VTD and FTD). In northern and central European regions (Köppen- Geiger Dfb, Dfc and ET climates), the variable threshold methods (VTD and VTM) generally detect drought earlier (March-July) than the fixed thresholds (FTD and FTM) (July-October). In the western European regions and the Mediterranean differences in timing among identification approaches are not so clear. The characteristics of SSI-1 drought, in general, are close to what is being identified with the VTM approach. Differences in drought characteristics highlight the importance of whether end-users should take seasonality into account or not (VT and SSI-1 versus FT) and consider temporal variability (daily versus monthly). Certainly, there is no unique hydrological drought definition that fits all purposes; hence we suggest that users should clearly agree among themselves upon a sharp definition on which type of streamflow drought is required to be identified for a specific application.

The FRIEND-Water program (FWP) is the oldest and the most transverse program (i.e. Flagship Initiative) within the Hydrological Intergovernmental Program (IHP) from UNESCO. FRIEND means “Flow Regimes from International and Experimental Network Data”. The FWP is dedicated to allow large communities of hydrologists and associated disciplines to collaborate across borders on common shared data and scientific topics which are addressed through large world regions. The program has evolved in its 35 years of existence. There are 8 large regions in the world which gather tenths of hundreds of researchers, and organize events following several research topics defined according to each region priorities. The FWP is chaired by a scientific committee gathering regional coordinators and thematic experts. Each region gives its research priorities which evolve according to the projections given by the member States during the IHP council every year. The future activities of the FWP are defined by the upcoming IHP IX program and with the support of the newly created Montpellier UNESCO Category II Center ICIREWARD, acting as FWP Secretariat.

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.

Robust sub-seasonal and seasonal drought forecasts are essential for water managers and stakeholders coping with water scarcity. Many studies have been conducted to evaluate the performance of hydrological forecasts, that is, streamflow. Nevertheless, only few studies evaluated the performance of hydrological drought forecasts, e.g. forecast of deficit volumes in river flow, or duration of deficits. The objective of this study, therefore, was to analyse the skill and robustness of meteorological and hydrological drought forecasts at the catchment scale (the Ter and Llobregat rivers in Catalonia, Spain), rather than at a continental or global scale. Meteorological droughts were forecasted using downscaled (5 km) probabilistic seasonal weather reforecasts (ECMWF-SEAS4). These downscaled data were also used to drive the hydrological model (EFAS-LISFLOOD) to produce hydrological drought forecasts, which were derived from time series of simulated streamflow data. This resulted in seasonal hydro-meteorological reforecasts with a lead time up to 7 months, for the time period 2002-2010. These monthly reforecasts were compared to two datasets: 1) droughts derived from a proxy for observed data, including gridded precipitation data and streamflow simulated by the LISFLOOD model; and 2) droughts derived from in situ observed precipitation and streamflow. Results show that the skill of hydrological drought forecasts is higher than the climatology, up to 3-4 months lead time. On the contrary, meteorological drought forecasts, analysed using the Standardized Precipitation Index (SPI), do not show added value for short accumulation times (SPI1 and SPI3). The robustness analysis show that using either a less extreme or a more extreme threshold leads to a large change in forecasting skill, which points at a rather low robustness of the hydrological drought forecasts. Because the skill found in hydrological drought forecasts is higher than the meteorological ones in this case study, the use of hydrological drought forecasts in Catalonia is highly recommended to improve drought risk management. The results of this study have already been implemented by the Catalonian Water Agency to forecast reservoir volumes of two big reservoirs located in the Ter and Llobregat catchments, which supply the majority of water to the Barcelona metropolitan area.

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 the 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.

Summarization: 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). Presented on: Advances in Water Resources

Summarization: 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). Presented on: Science of the Total Environment

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 5×5 km) 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.

In the Anthropocene, hydrological processes and the state of water in different parts of the terrestrial phase of the hydrological cycle can be altered both directly and indirectly due to human interventions and natural phenomena. Adaption and mitigation of future severe droughts need precise insights into the natural and anthropogenic drivers of droughts and understanding how variability in human drivers can alter anthropogenic droughts in positive or negative ways. The aim of the current study was expanding the &ldquo, observation-modelling&rdquo, approach to quantify different types of natural and human droughts. In addition, quantifying enhanced or alleviated modified droughts was the second parallel purpose of the research. The main principle of this approach is the simulation of the condition that would have happened in the absence of human interventions. The extended approach was tested in two Iranian catchments with notable human interventions and different climatic conditions. The drought events were identified through hydrological modelling by the Hydrologiska Byr&aring, ns Vattenbalansavdelning (HBV) model, naturalizing the time series of hydrometeorological data for a period with no significant human interventions, and anomaly analysis. The obtained results have demonstrated that both catchments were almost the same in experiencing longer and more severe negative modified droughts than positive ones because of the negative pressure of human activities on the hydrological system. A large number of natural droughts have also been transformed into modified droughts because of the intensive exploitation of surface and sub-surface water resources and the lack of hydrological system recovery. The results show that the extended approach can detect and quantify different drought types in our human-influenced era.

Quantifying the influence of human activities, such as reservoir building, water abstraction, and land use change, on hydrology is crucial for sustainable future water management, especially during drought. Model-based methods are very time-consuming to set up and require a good understanding of human processes and time series of water abstraction, land use change, and water infrastructure and management, which often are not available. Therefore, observation-based methods are being developed that give an indication of the direction and magnitude of the human influence on hydrological drought based on limited data. We suggest adding to those methods a “paired-catchment” approach, based on the classic hydrology approach that was developed in the 1920s for assessing the impact of land cover treatment on water quantity and quality. When applying the paired-catchment approach to long-term pre-existing human influences trying to detect an influence on extreme events such as droughts, a good catchment selection is crucial. The disturbed catchment needs to be paired with a catchment that is similar in all aspects except for the human activity under study, in that way isolating the effect of that specific activity. In this paper, we present a framework for selecting suitable paired catchments for the study of the human influence on hydrological drought. Essential elements in this framework are the availability of qualitative information on the human activity under study (type, timing, and magnitude), and the similarity of climate, geology, and other human influences between the catchments. We show the application of the framework on two contrasting case studies, one impacted by groundwater abstraction and one with a water transfer from another region. Applying the paired-catchment approach showed how the groundwater abstraction aggravated streamflow drought by more than 200 % for some metrics (total drought duration and total drought deficit) and the water transfer alleviated droughts with 25 % to 80 %, dependent on the metric. Benefits of the paired-catchment approach are that climate variability between pre- and post-disturbance periods does not have to be considered as the same time periods are used for analysis, and that it avoids assumptions considered when partly or fully relying on simulation modelling. Limitations of the approach are that finding a suitable catchment pair can be very challenging, often no pre-disturbance records are available to establish the natural difference between the catchments, and long time series of hydrological data are needed to robustly detect the effect of the human activities on hydrological drought. We suggest that the approach can be used for a first estimate of the human influence on hydrological drought, to steer campaigns to collect more data, and to complement and improve other existing methods (e.g. model-based or large-sample approaches).

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. Keywords: Multi hazard, Historical data, Concurrent events, Sequent events

Vast areas of wetlands in Southeast Asia are undergoing a transformation process to human-modified ecosystems. Expansion of agricultural cropland and forest plantations changes the landscape of wetlands. Here we present observation-based modelling evidence of increased fire hazard due to canalization in tropical wetland ecosystems. Two wetland conditions were tested in South Sumatra, Indonesia, natural drainage and canal drainage, using a hydrological model and a drought-fire index (modified Keetch–Byram index). Our results show that canalization has amplified fire susceptibility by 4.5 times. Canal drainage triggers the fire season to start earlier than under natural wetland conditions, indicating that the canal water level regime is a key variable controlling fire hazard. Furthermore, the findings derived from the modelling experiment have practical relevance for public and private sectors, as well as for water managers and policy makers, who deal with canalization of tropical wetlands, and suggest that improved water management can reduce fire susceptibility.

Understanding, characterizing, and predicting drought is vital for the reduction of its consequences. In the last few decades, many studies have moved drought analysis from the conventional lumped approach to a more spatiotemporal analysis. Two main developments have motivated this: one is global data availability and the other is the number of models developed to understand and quantify drought. The first one relates to information available from reanalysis products, and the second regards global and regional, distributed and semidistributed model data. Moreover, nowadays, different organizations provide drought monitoring information in near real time. However, a few spatiotemporal analysis studies have been developed slowly and the availability of comprehensive tools is still limited. This chapter proposes a new toolbox that performs the Spatio-Temporal ANalysis of Drought (STAND) in MATLAB, step by step. The toolbox collects some of the applications of previous studies and innovates new concepts on the characterization of drought. The methodologies here allow estimation of drought duration, severity (magnitude), and area, redefining the drought event in space and time. A key component in the analysis is the visualization of outcomes, as well as the spatial interpolation of pointwise data. The proposed STAND toolbox is explained and its use is illustrated with two large-scale examples (India and Mexico). The results have been compared with local reported information. STAND outcomes have been shown to help follow space–time events in terms of patterns, and provide information related to the characterization of extremes for drought analysis.

Drought management in the anthropocene is challenging because of the complex interaction between climate variability, land surface processes and human changes in the hydrologic system. In the current research, different kinds of natural and human-affected droughts in the anthropocene, including climate-induced drought, human-induced drought and human-modified drought have been investigated for two Iranian catchments (Kiakola and Eskandari catchments), as an example. We propose a methodology to distinguish and quantify positive and negative human-modified droughts to explore the impact of human interferences on river flow and groundwater. The methodology uses naturalized conditions obtained by simulation modelling as a reference to distinguish the droughts. Positive human-modified droughts happen when human activities alleviate natural droughts, whereas negative human-modified droughts reflect severer drought conditions than naturally occurring. Application of the methodology shows that human activities mostly caused negative human-modified droughts (e.g. decreased river flow in more than 89 % of the events) in the selected Iranian catchments, where the Eskandari catchment had about four times severer human-modified droughts than the Kiakola catchment. Positive human-modified droughts mostly occurred in the wet season, which cannot counterbalance the adverse impacts of the negative human-modified droughts occurring in the following dry season. The proposed methodology enables further quantification of drought in the anthropocene through evaluation of negative and positive effects of human activates on the hydrologic system, which will support water management to reduce drought impacts.

International audience; It is generally accepted that drought is one of the most costly weather-related natural hazards. In 2015, a long-lasting drought hit Europe, particularly affecting central and eastern Europe. In some regions it was the driest (North Slovakia) and in others (Czech Republic and Poland) it was the second driest summer of the last 50 years (following 2003). Key questions are: (i) how extreme are these events, not only in terms of hydro-meteorological characteristics but also impacts? and (ii) how are these impacts managed?

Lately, drought is more intense and much more severe around the globe, causing more deaths than other hazards in the past century. Drought can be characterized quantitatively for its spatial extent, intensity and duration by using drought indicators. Several indicators have been developed in order to characterize drought, being the most widespread the Standardized Precipitation Index (SPI). Nevertheless, due to its known limitations, other indicators have been proposed. In this paper, evaporation and runoff simulations of a basin were used to evaluate the variation and performance of different meteorological and hydrological drought indicators in identifying drought. Daily simulations of evaporation and runoff were computed by using a distributed hydrological model of a catchment located in the southeast of Mexico. After calibration of the hydrological model, we calculated at different time steps the drought indicators: Standardized Precipitation Index (SPI), Standardized Precipitation Evaporation Index (SPEI), Evapotranspiration Deficit Index (ETDI), Standardized Evapotranspiration Deficit Index (SEDI) and Standardized Runoff Index (SRI). Furthermore, the so-called Non-Contiguous Drought Analysis (NCDA) was carried out to compare the skill of each indicator to identify drought. Results show that meteorological drought indicators do not identify all drought events for the time steps of 1 and 3 months. For 3-, 6- and 9-month time steps, meteorological drought indicators tend to identify the onset with a lag. For long-time steps of 12 and 24, the use of agricultural and hydrological droughts indicators is recommended, since these indicators can identify prolonged drought periods. The results suggest that for a better monitoring of drought in a catchment, it is important the joint evaluation and the use of not only meteorological drought indicators but also hydrological and agricultural ones, in order to identify drought events and their spatio-temporal evolution.

Robust sub-seasonal and seasonal drought forecasts are essential for water managers and stakeholders coping with water shortage. Many studies have been conducted to evaluate the performance of hydrological forecasts, that is, streamflow. Nevertheless, only few studies evaluated the performance of hydrological drought forecasts. The objective of this study, therefore, is to analyse the skill and robustness of meteorological and hydrological drought forecasts on a catchment scale (the Ter and Llobregat rivers in Catalonia, Spain), rather than on a continental or global scale. Meteorological droughts were forecasted using downscaled (5 km) probabilistic weather reforecasts (ECMWF-SEAS4). These downscaled data were also used to produce hydrological drought forecasts, derived from time series of streamflow data simulated with a hydrological model (LISFLOOD). This resulted in seasonal hydro-meteorological reforecasts with a lead time up to 7 months, for the time period 2002–2010. These monthly reforecasts were compared to two datasets: (1) droughts derived from a proxy for observed data, including gridded precipitation data and discharge simulated by the LISFLOOD model, fed by these gridded climatological data; and (2) droughts derived from in situ observed precipitation and discharge. Results showed that the skill of hydrological drought forecasts is higher than the climatology, up to 3–4 months lead time. On the contrary, meteorological drought forecasts, analysed using the Standardized Precipitation Index (SPI), do not show added value for short accumulation times (SPI1 and SPI3). The robustness analysis showed that using either a less extreme or a more extreme threshold leads to a large change in forecasting skill, which points at a rather low robustness of the hydrological drought forecasts. Because the skill found in hydrological drought forecasts is higher than the meteorological ones in this case study, the use of hydrological drought forecasts in Catalonia is highly recommended for management of water resources. Keywords: Hydro-meteorological droughts, Catchment scale, Forecasting skill, Sensitivity analysis

Borneo’s diverse ecosystems, which are typical humid tropical conditions, are deteriorating rapidly, as the area is experiencing recurrent large-scale wildfires, affecting atmospheric composition1, 2, 3, 4 and influencing regional climate processes5, 6. Studies suggest that climate-driven drought regulates wildfires2, 7, 8, 9, but these overlook subsurface processes leading to hydrological drought, an important driver. Here, we show that models which include hydrological processes better predict area burnt than those solely based on climate data. We report that the Borneo landscape10 has experienced a substantial hydrological drying trend since the early twentieth century, leading to progressive tree mortality, more severe than in other tropical regions11. This has caused massive wildfires in lowland Borneo during the past two decades, which we show are clustered in years with large areas of hydrological drought coinciding with strong El Niño events. Statistical modelling evidence shows amplifying wildfires and greater area burnt in response to El Niño/Southern Oscillation (ENSO) strength, when hydrology is considered. These results highlight the importance of considering hydrological drought for wildfire prediction, and we recommend that hydrology should be considered in future studies of the impact of projected ENSO strength, including effects on tropical ecosystems, and biodiversity conservation.

Over the last decades, the global population has been rapidly increasing and human activities have altered terrestrial water fluxes at an unprecedented scale. 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 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 large-scale 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 are incorporating human impacts on hydrological cycle, yet human representations in hydrological models remain challenging. In this paper we provide a synthesis of progress in the development and application of human impact modeling 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.

Drought is an abnormal and prolonged deficit in available water. Possible drought impacts are crop losses, famine, fatalities, power blackouts and degraded ecosystems. These severe socio-economic and environmental impacts show the need to carefully monitor drought conditions using a suitable index. Our objective is to provide an intercomparison of frequently used physical drought indices to show to which degree they are interchangeable for monitoring drought in precipitation, soil moisture, groundwater and streamflow. Physical indices are commonly introduced to predict drought impacts, because appropriate drought impact indices are still missing. Correlations (R) between frequently used indices for different drought types were calculated at the global scale. We have made the index timeseries available to the community for future studies. Precipitation drought indices show low to intermediate correlations (ranging from R = 0.1 to 0.75), soil moisture drought indices show an even lower similarity (R = 0.25). Indices for streamflow drought show the highest correlation (R = 0.5 to 0.95). Additionally, meteorological drought indices do not capture the soil moisture drought correctly (R = 0.0 to 0.6) nor streamflow drought (R = 0.0 to 0.7). These findings have implications for drought monitoring systems: (i) for each drought type, a different index should carefully be identified; (ii) drought indices that are designed to monitor the same drought type show large discrepancies in their anomalies and hence drought detection; (iii) there is no single superior physical drought index that is capable of accurately capturing the diverse set of drought impacts in all parts of the hydrological cycle.

The summer drought of 2015 affected a large portion of continental Europe and was one of the most severe droughts in the region since summer 2003. The summer of 2015 was characterized by exceptionally high temperatures in many parts of central and eastern Europe, with daily maximum temperatures 2 °C higher than the seasonal mean (1971–2000) over most of western Europe, and more than 3 °C higher in the east. It was the hottest and climatologically driest summer over the 1950–2015 study period for an area stretching from the eastern Czech Republic to Ukraine. For Europe, as a whole, it is among the six hottest and driest summers since 1950. High evapotranspiration rates combined with a lack of precipitation affected soil moisture and vegetation and led to record low river flows in several major rivers, even beyond the drought-hit region. The 2015 drought developed rather rapidly over the Iberian Peninsula, France, southern Benelux and central Germany in May and reached peak intensity and spatial extent by August, affecting especially the eastern part of Europe. Over the summer period, there were four heat wave episodes, all associated with persistent blocking events. Upper-level atmospheric circulation over Europe was characterized by positive 500 hPa geopotential height anomalies flanked by a large negative anomaly to the north and west (i.e., over the central North Atlantic Ocean extending to northern Fennoscandia) and another center of positive geopotential height anomalies over Greenland and northern Canada. Simultaneously, the summer sea surface temperatures (SSTs) were characterized by large negative anomalies in the central North Atlantic Ocean and large positive anomalies in the Mediterranean basin. Composite analysis shows that the western Mediterranean SST is strongly related to the occurrence of dry and hot summers over the last 66 years (especially over the eastern part of Europe). The lagged relationship between the Mediterranean SST and summer drought conditions established in this study can provide valuable skill for the prediction of drought conditions over Europe on interannual to decadal timescales.

Hydrologické modely sú nástrojmi, často využívanými pri riešení širokého spektra hydrologických problémov. Každý z modelov má svoju vlastnú štruktúru, definujúcu vzájomné vzťahy prvkov hydrologickej bilancie. Preto musí byť pri posudzovaní rozdielnych výsledkov získaných použitím tých istých vstupných dát brané do úvahy porovnanie rozdielov vo vnútornej štruktúre modelov. V príspevku boli porovnávané výsledky simulácie podzemného odtoku tromi rozličnými modelmi BILAN, FRIER a HBV-light, berúc do úvahy znalosti o vnútornej štruktúre jednotlivých modelov. Bolo dokumentované, že priebehy modelovaných parametrov sú veľmi podobné, no získané hodnoty sa líšia. Najvyššie hodnoty podzemného odtoku boli simulované modelom BILAN, v dôsledku faktu, že v modeli je zabudovaná pevná limitná hodnota pre veľkosť zásoby vody v pôde. Najnižšie hodnoty podzemného odtoku boli získané modelom HBV-light. Simulované hodnoty podzemného odtoku boli porovnané s priebehom úrovne hladiny podzemnej vody a s minimálnymi mesačnými prietokmi. Toto porovnanie ukázalo, že hodnoty podzemného odtoku v povodí Nitry po profil Nedožery simulované modelmi BILAN a FRIER sú bližšie k reálnemu stavu než hodnoty simulované modelom HBV-light.

The summer drought of 2015 affected a large portion of continental Europe and was one of the most severe droughts in the region since summer 2003. The summer was characterized by exceptionally high temperatures in many parts of central and eastern Europe, with daily maximum temperatures 2 °C warmer than the seasonal mean (1971–2000) over most of western Europe, and more than 3 °C warmer in the east. High evapotranspiration rates combined with a lack of precipitation affected the soil moisture content and vegetation stress and led to record low river flows in several major European rivers. This paper analyses the European summer drought of 2015 from a climatological perspective, including its origin, spatial and temporal development, and how it compares with the 2003 event. It discusses the main contributing factors controlling the occurrence and persistence of the event: temperature and precipitation anomalies, blocking episodes and sea surface temperatures (SST). Central Europe experienced during the summer of 2015 widespread areas of negative Standardized Precipitation Index (SPI) and Standardized Precipitation-Evapotranspiration Index (SPEI) values, present from May onwards. Similar to the summer drought of 2003, the upper level atmospheric circulation over Europe was characterized by a positive 500 hPa geopotential height anomalies flanked by a large negative anomaly to the north and west (i.e. over the central North Atlantic Ocean extending to northern Fennoscandia) and another center of positive geopotential height anomalies over Greenland and northern Canada. Simultaneously, the summer SST was characterized by large negative anomalies in the central North Atlantic Ocean and large positive anomalies in the Mediterranean basin. The SST dipole-like anomalies between the central Atlantic and the Mediterranean Sea suggest a possible "atmospheric teleconnection" between the two regions, which in turn may affect the drought conditions over Europe. In an accompanying paper, the hydrological perspective of the summer 2015 drought is presented. Together, these two papers summarize a collaborative initiative of members of UNESCO’s FRIEND-Water program to perform a timely Pan-European assessment of the 2015 summer drought.

Droughts are having major impacts around the world. Effective drought management is impeded by a lack of understanding of feedbacks between drought and people. In this human-influenced era, we need to rethink the concept of drought to include the role of people in mitigating or enhancing drought

Drought is a natural hazard that can cause a wide range of impacts affecting the environment, society, and the economy. Assessing and reducing vulnerability to these impacts for regions beyond the local scale, spanning political and sectoral boundaries, requires systematic and detailed data regarding impacts. This study presents an assessment of the diversity of drought impacts across Europe based on the European Drought Impact report Inventory (EDII), a unique research database that has collected close to 5000 impact reports from 33 European countries. The reported drought impacts were classified into major impact categories, each of which had a number of subtypes. The distribution of these categories and types was then analyzed over time, by country, across Europe and for particular drought events. The results show that impacts on agriculture and public water supply dominate the collection of drought impact reports for most countries and for all major drought events since the 1970s, while the number and relative fractions of reported impacts in other sectors can vary regionally and from event to event. The data also shows that reported impacts have increased over time as more media and website information has become available and environmental awareness has increased. Even though the distribution of impact categories is relatively consistent across Europe, the details of the reports show some differences. They confirm severe impacts in southern regions (particularly on agriculture and public water supply) and sector-specific impacts in central and northern regions (e.g. on forestry or energy production). As a text-based database, the EDII presents a new challenge for quantitative analysis; however, the EDII provides a new and more comprehensive view on drought impacts. Related studies have already developed statistical techniques to evaluate the link between drought indices and impacts using the EDII. The EDII is a living database and is a promising source for further research on drought impacts, vulnerabilities, and risks across Europe. A key result is the extensive variety of impacts found across Europe and its documentation. This data coverage may help drought policy planning at national to international levels.

In the current human-modified world, or Anthropocene, the state of water stores and fluxes has become dependent on human as well as natural processes. Water deficits (or droughts) are the result of a complex interaction between meteorological anomalies, land surface processes, and human inflows, outflows, and storage changes. Our current inability to adequately analyse and manage drought in many places points to gaps in our understanding and to inadequate data and tools. The Anthropocene requires a new framework for drought definitions and research. Drought definitions need to be revisited to explicitly include human processes driving and modifying soil moisture drought and hydrological drought development. We give recommendations for robust drought definitions to clarify timescales of drought and prevent confusion with related terms such as water scarcity and overexploitation. Additionally, our understanding and analysis of drought need to move from single driver to multiple drivers and from uni-directional to multi-directional. We identify research gaps and propose analysis approaches on (1) drivers, (2) modifiers, (3) impacts, (4) feedbacks, and (5) changing the baseline of drought in the Anthropocene. The most pressing research questions are related to the attribution of drought to its causes, to linking drought impacts to drought characteristics, and to societal adaptation and responses to drought. Example questions include (i) What are the dominant drivers of drought in different parts of the world? (ii) How do human modifications of drought enhance or alleviate drought severity? (iii) How do impacts of drought depend on the physical characteristics of drought vs. the vulnerability of people or the environment? (iv) To what extent are physical and human drought processes coupled, and can feedback loops be identified and altered to lessen or mitigate drought? (v) How should we adapt our drought analysis to accommodate changes in the normal situation (i.e. what are considered normal or reference conditions) over time? Answering these questions requires exploration of qualitative and quantitative data as well as mixed modelling approaches. The challenges related to drought research and management in the Anthropocene are not unique to drought, but do require urgent attention. We give recommendations drawn from the fields of flood research, ecology, water management, and water resources studies. The framework presented here provides a holistic view on drought in the Anthropocene, which will help improve management strategies for mitigating the severity and reducing the impacts of droughts in future.

Time-series for river gauging stations are core blue-skies and applied research resources for understanding impacts of climate and anthropogenic change on basin hydrology. River flow archives hold vital information for evidence-based assessment of past hydrological variability, and support hydrological modelling of future changes. River discharge is an integration of basin input, storage and transfer processes to the gauging point. It is important to set basin outlet data in regional to global and long-term contexts: to better understand nested scales of variability; to pinpoint locations and time periods most sensitive to climate and human impacts; to make predictions for ungauged basins; and to inform decision makers on water security issues, and where and when to take measures to mitigate water hazards and stress, including floods and droughts (Dai et al., 2009; Bonnell et al., 2006; Feyen & Dankers, 2009; Haddeland et al., 2006; Hannah et al., 2005). Thus, there is clear rationale for supporting large-scale (i.e. regional to continental to global) river flow archives. Notable examples of such databases include that held by the WMO Global Runoff Data Centre (GRDC) and the UNESCO Flow Regimes from International Experimental and Network Data (FRIEND) European Water Archive (EWA). For large-scale river flow archives to be valuable research resources, they must be fit for purpose. However, these databases are at risk due to a possible decline in network coverage, associated time-series truncation, growing human impact on (near-) natural flows, and increasingly restricted access to national-scale data. This commentary aims: (1) to demonstrate largescale river flow datasets are crucial to advance hydrological science and solve operational issues; (2) to assess the current status of large-scale river flow datasets; and (3) to propose ways forward to consolidate historical data and secure future river flow data.