Droughts are often long-lasting phenomena, without a distinct start or end and with impacts cascading across sectors and systems, creating long-term legacies. Nevertheless, our current perceptions and management of droughts and their impacts are often event-based, which can limit the effective assessment of drought risks and reduction of drought impacts. Here, we advocate for changing this perspective and viewing drought as a hydrological–ecological–social continuum. We take a systems theory perspective and focus on how “memory” causes feedback and interactions between parts of the interconnected systems at different timescales. We first discuss the characteristics of the drought continuum with a focus on the hydrological, ecological, and social systems separately, and then we study the system of systems. Our analysis is based on a review of the literature and a study of five cases: Chile, the Colorado River basin in the USA, northeast Brazil, Kenya, and the Rhine River basin in northwest Europe. We find that the memories of past dry and wet periods, carried by both bio-physical (e.g. groundwater, vegetation) and social systems (e.g. people, governance), influence how future drought risk manifests. We identify four archetypes of drought dynamics: impact and recovery, slow resilience building, gradual collapse, and high resilience–big shock. The interactions between the hydrological, ecological, and social systems result in systems shifting between these types, which plays out differently in the five case studies. We call for more research on drought preconditions and recovery in different systems, on dynamics cascading between systems and triggering system changes, and on dynamic vulnerability and maladaptation. Additionally, we advocate for more continuous monitoring of drought hazards and impacts, modelling tools that better incorporate memories and adaptation responses, and management strategies that increase societal and institutional memory. This will help us to better deal with the complex hydrological–ecological–social drought continuum and identify effective pathways to adaptation and mitigation.

Hydrological models are essential for predicting water flux dynamics, including extremes, and managing water resources, yet traditional process-based large-scale models often struggle with accuracy and process understanding due to their inability to represent complex, non-linear hydrometeorological processes, limiting their effectiveness in local conditions. Here we explore hybrid methods combining process-based modelling and statistical or machine learning post-processors to improve streamflow predictive accuracy, including extremes, across Europe’s hydro-climatic gradient. We investigate various post-processing methods, such as random forest, long short-term memory model, quantile mapping and generalised linear model, demonstrating notable improvements in model performance, in terms of reducing errors in total volume and extremes and increasing robustness across diverse climatic and geographic conditions. We further show that hydrologic similarity is one of the key drivers that control the hybrid approach’s improvements, together with other basin characteristics, such as mean precipitation and mean temperature. Our results also reveal spatial complementarity among the post-processing methods, with no absolute superiority identified from a single method, pointing towards multi-model averaging approaches for the future evolution of hybrid hydrological modelling.

Due to the large amount of water resources stored in glaciers, permafrost, and lakes, the source region of the Yangtze River (SRYR) is of great importance for the overall basin water flow. For this purpose, a state of art review and calculations were made for the period 1957–2013 using observed hydrological and meteorological data with a water balance approach. Actual evapotranspiration was calculated and validated by empirical formulas. Water storage change analysis was conducted with uncertainty boundaries using a 10-year moving window. Results show that temperature, precipitation, and actual evapotranspiration in the SRYR increased by 0.34 °C, 11.4 mm, and 7.6 mm per decade, respectively (significant at 0.05 probability level). Runoff appears to have increased at a rate of 3.3 mm per decade. The SRYR water storage in total has not changed significantly during the period, although the moving average is mostly below zero. Based on the water balance equation, the increase in calculated evapotranspiration is mainly due to the significantly increasing temperature. This in combination with increasing precipitation leads to a relatively stable water storage during the study period. Correlation analyses show that precipitation dominates runoff during the warm season (May to October), while temperature anomalies dominate the runoff during the cold season (November to April). The influence of temperature on runoff seems to enhance during the winter period.

Temperature and precipitation extremes are the dominant causes of natural disasters. In this study, seven indices of extreme temperature and precipitation events in Gansu Province, China, were analysed for the period 1961–2017. An abrupt climate shift was recorded during 1980–1981. Thus, the study period was divided into a preshift (before the climate shift) period 1961–1980 and an aftshift (after the climate shift) period 1981–2017. Comparison of mean extreme indices for preshift and aftshift periods was performed for the purpose of exploring possible increasing/decreasing patterns. Generalized extreme value (GEV) distribution was applied spatially to fit the extreme indices with return periods up to 100 years for preshift/aftshift periods. Singular value decomposition (SVD) was adopted to investigate possible correlation between the extreme climate events and indices of large-scale atmospheric circulation. The results indicate that changes in mean and return levels between the preshift and aftshift periods vary significantly in time and space for different extreme indices. Increase in extreme temperature regarding magnitude and frequency for the aftshift period as compared with the preshift period suggests a change to a warmer and more extreme climate during recent years. Changes in precipitation extremes were different in southern and northern parts of Gansu. The precipitation extremes in the north have increased that can result in more serious floods and droughts in the future. SVD analyses revealed a complex pattern of correlation between climate extremes and indices of large-scale atmospheric circulation. Strengthening of westerlies and weakening of the south summer monsoon contribute to the complex changing patterns of precipitation extremes. Results in this study will contribute to disaster risk prevention and better water management in this area.

Advances in hydrological modeling and numerical weather forecasting have allowed hydro-climate services to provide accurate impact simulations and skillful forecasts that can drive decisions at the local scale. To enhance early warnings and long-term risk reduction actions, it is imperative to better understand the hydrological extremes and explore the drivers for their predictability. Here, we investigate the seasonal forecast skill of streamflow extremes over the pan-European domain, and further attribute the discrepancy in their predictability to the local river system memory as described by the hydrological regimes. Streamflow forecasts at about 35 400 basins, generated from the E-HYPE hydrological model driven with bias-adjusted ECMWF SEAS5 meteorological forcing input, are explored. Overall the results show adequate predictability for both hydrological extremes over Europe, despite the spatial variability in skill. The skill of high streamflow extreme deteriorates faster as a function of lead time than that of low extreme, with a positive skill persisting up to 12 and 20 weeks ahead for high and low extremes, respectively. A strong link between the predictability of extremes and the underlying local hydrological regime is identified through comparative analysis, indicating that systems of analogous river memory, e.g. fast or slow response to rainfall, can similarly predict the high and low streamflow extremes. The results improve our understanding of the geographical areas and periods, where the seasonal forecasts can timely provide information on very high and low streamflow conditions, including the drivers controlling their predictability. This consequently benefits regional and national organizations to embrace seasonal prediction systems and improve the capacity to act in order to reduce disaster risk and support climate adaptation.

Water and disaster risk management require accurate information about hydrometeorological extremes. However, estimation of rare events using extreme value analysis is hampered by short observational records, with large resulting uncertainties. Here, we present a surrogate world setup that makes use of data samples from meteorological and hydrological seasonal re‐forecasts to explore extremes for long return periods. The surrogate timeseries allow us to pool the re‐forecasts into 1000‐year‐long timeseries. We can then calculate return values of extremes and explore how they are affected by the size of sub‐samples as method for estimating the uncertainty. The approach relies on the fact that probabilistic seasonal re‐forecasts, initialized with perturbed initial conditions, have limited predictive skill with increasing lead time. At long lead times re‐forecasts will diverge into independent samples. The meteorological seasonal re‐forecasts are taken from the SEAS5 system, and hydrological re‐forecasts are generated with the E‐HYPE process‐based model for the pan‐European domain. Extreme value analysis is applied to annual maxima of precipitation and streamflow for return periods of 100 years. The analysis clearly demonstrates the large uncertainty in long return period estimates with typical available samples of only few decades. The uncertainty is somewhat reduced for 100‐year samples, but several 100 years seem to be necessary to have robust estimates. The bootstrap with replacement approach is applied to shorter timeseries, and is shown to well reproduce the uncertainty range of the longer samples. However, the main estimate of the return value can be significantly offset. Although the method is model based, with the associated uncertainties and bias compared to the real world, the surrogate approach is likely useful to explore rare and compounding extremes.