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This paper addresses the question of how catchment-scale water and energy balances have responded to climatic and land surface changes over the last 70 years in the federal state of Saxony in eastern Germany. Therefore, observational data of hydrological and meteorological monitoring sites from 1951 to 2020 across 71 catchments are examined in a relative water- and energy-partitioning framework to put the recent drought-induced changes into a historical perspective. A comprehensive visualization method is used to analyze the observed time series. The study focuses on changes on a decadal timescale and finds the largest decline in observed runoff in the last decade (2011–2020). The observed decline can be explained by the significant increase in aridity, caused by the reduction in annual mean rainfall and a simultaneous increase in potential evaporation. In a few mainly forested headwater catchments, the observed decline in runoff was even stronger than predicted by climate conditions alone. These catchments are still recovering from past widespread forest damages sustained in the 1970s to 1980s, resulting in a continuous increase in actual evapotranspiration due to forest regrowth. On the contrary, runoff stayed almost constant in other catchments despite an increase in aridity. These results highlight that water budgets in Saxony are in an unstable, non-stationary regime due to significant climatic changes and the regional impacts of land surface changes such as forest health. The recent decreases in the mean annual runoff are substantial and must be taken into account by the authorities for freshwater management. [ABSTRACT FROM AUTHOR]

There is a scientific consensus that the Mediterranean region (MedR) is warming and as the temperature continues to rise, droughts and heat waves are becoming more frequent, severe, and widespread. Given the detrimental effects of droughts, it is crucial to accelerate the development of forecasting and early warning systems to minimize their negative impact. This paper reviews the current state of drought modeling and prediction applied in the MedR, including statistical, dynamical, and hybrid statistical–dynamical models. By considering the multifaceted nature of droughts, the study encompasses meteorological, agricultural, and hydrological drought forms and spans a variety of forecast scales, from weekly to annual timelines. Our objective is to pinpoint the knowledge gaps in literature and to propose potential research trajectories to improve the prediction of droughts in this region. The review finds that while each method has its unique strengths and limitations, hybrid statistical–dynamical models appear to hold the most promising potential for skillful prediction with seasonal to annual lead times. However, the application of these methods is still challenging due to the lack of high-quality observational data and the limited computational resources. Finally, the paper concludes by discussing the importance of using a combination of sophisticated methods such as data assimilation techniques, machine learning models, and copula models and of integrating data from different sources (e.g., remote sensing data, in situ measurements, and reanalysis) to improve the accuracy and efficiency of drought forecasting. [ABSTRACT FROM AUTHOR]

A cascade of drought impacts refers to a series of interconnected events that trigger a chain reaction of impacts, extending beyond water scarcity, to affect agricultural production, socio-economic factors, and the environment. This paper aims to understand the role of society in mitigating drought impacts, particularly through policy responses. Conducting a case study in Ceará state, northeast Brazil, we used a global rare dataset of continuously drought monitoring, complemented by interviews with smallholder farmers and agricultural extension technicians. Additionally, we analyzed policy documents related to public policies implemented at the local level. Employing a classification of drought impacts as our analytical framework, our findings indicate that socio-environmental-economic impacts of drought are less frequently reported, suggesting that development policies are mitigating cascading effects on livelihoods. Most impacts are associated with hydrological impacts of drought, suggesting unintended consequences of investments in increasing water supply. We emphasize the significant contribution of public policies to mitigating the cascading effects of drought, which do not necessarily involve increasing water availability, but strengthen the local economy. [ABSTRACT FROM AUTHOR]

The water supply of water resources allocation under extreme drought is insufficient, and the limited available water resources make it urgent to tap the potential of water supply. In this paper, the Yellow River water supply area in Henan Province is taken as an example to study the multi-water source allocation under extreme drought. According to the Palmer Drought Severity Index (PDSI), the extreme drought years are selected, and the water supply and demand balance in the extreme drought years is analyzed, and the water shortage degree of each water supply area is obtained. In this paper, unconventional water, flood resource utilization and elastic exploitation of groundwater are used as potential water sources. Different water supply scenarios are set up according to different potential tapping measures, and multi-scenario supply increase under extreme drought is explored. A multi-water source allocation model with the goal of minimizing water shortage is constructed, and a multi-scenario supply increase allocation scheme is proposed, which provides a basis for the study of water supply increase allocation to alleviate the drought degree of the the Yellow River Water Supply Area in Henan. Through the Multi-scenario potential tapping of multiple water sources, the existing potential water volume can be maximized, which is conducive to reducing the water supply pressure and water use restrictions of conventional water sources, improving the support capacity and guarantee capacity of water resources, and reducing the economic and social development bottlenecks caused by extreme drought. [ABSTRACT FROM AUTHOR]

Understanding past and present hydro-system feedbacks to global ocean-atmospheric interactions represents one of the main challenges to preventing droughts, extreme events and related human catastrophes in the face of global warming, especially in arid and semiarid environments. In eastern Africa, the El Niño-Southern Oscillation (ENSO) was identified as one of the primary drivers of precipitation variability affecting water availability. However, the northern East African Rift System (EARS) still suffers from ENSO climate teleconnection and the underrepresentation of predictive models because of the scarcity of local-to-regional historical or palaeo-data. In this paper, we provide a 50-year seasonal flood/drought chronicle of the Awash River catchment from the study of laminated sediment from Gemeri and Afambo lakes (Central Afar region, Ethiopia), with the aim of reconstructing the magnitude of regional hydro-climatic events. Pluri-centimetric micro-laminated lithogenic facies alternating with pluri-millimetric carbonate-enriched facies are investigated in both lakes. We couple dating methods including radiocarbon, short-lived radionuclides, palaeomagnetic field variations and varve counting on both lake deposits to build a high-resolution age model and to discuss the regional hydro-sedimentary dynamics of the Awash River over the last ~700 years, with a focus on the last fifty years. Using a multiproxy approach, we observe that following a multi-centennial enhanced hydrological period, the two lakes experienced a gradual decrease in river load inflow since 1979 CE, attaining extreme drought and high evaporative conditions between 1991 and 1997 CE. In 2014, the construction of a dam and the improvement of agricultural hydraulic management in the lower Awash River plain impacted the erodibility of local soils and the hydro-sedimentary balance of the lake basins, as evidenced by a disproportionate sediment accumulation rate. Comparison of our quantitative reconstruction with i) lake water surface evolution expressed in Km2, ii) the interannual Awash River flow rates expressed in mm/yr, and iii) the El Niño 3.4 model highlights the intermittent connections between ENSO SST anomalies, regional droughts and hydrological conditions in the northern EARS. [ABSTRACT FROM AUTHOR]

The Thirty Years' War, which took place from 1618 to 1648 CE, was an armed military conflict in Europe. It resulted from the culmination of theological differences between advocates of the Roman Catholic and Protestant churches, as well as a power struggle for European political hegemony. This war brought about extensive devastation to Europe. Based on documentary evidence, this paper characterizes the climate, weather extremes, and economic and socio-political events in central Europe during that time. Natural climate forcing indicates a gradual climate deterioration during the first half of the 17th century, associated with a decrease in solar activity towards the Maunder Minimum and increased volcanic activity. The mean temperatures in central Europe from 1618 to 1648 were significantly colder than the reference period of 1961 to 1990 in winter, autumn, and annually, while precipitation and drought means did not differ significantly from the reference period. Summer temperatures, spring precipitation, and drought also exhibited significantly greater variability. As for weather extremes, particularly late winter, late spring and early autumn frosts, floods, intense rain spells, and droughts affected grain, fruit, and vine grape harvests, as well as the yields of other crops. These weather extremes contributed to various human impacts, such as food shortages (reflecting harvests and grain prices), famines, and epidemics. Ultimately, these events, along with the effects of the war, led to a decline in the population. The results obtained are discussed within the broader European context, taking into account climate, weather extremes, and socio-economic impacts. [ABSTRACT FROM AUTHOR]

In this paper future changes of surface water availability in Austria are investigated. We use an ensemble of downscaled and bias-corrected regional climate model simulations of the EURO-CORDEX initiative under moderate mitigation (RCP4.5) and Paris Agreement (RCP2.6) emission scenarios. The climatic water balance and its components (rainfall, snow melt, glacier melt and atmospheric evaporative demand) are used as indicators of surface water availability, and we focus on different altitudinal classes (lowland, mountainous and high alpine) to depict a variety of processes in complex terrain. Apart from analysing the mean changes of these components, we also pursue a hazard risk approach by estimating future changes in return periods of meteorological drought events of a given magnitude as observed in the reference period. The results show, in general, wetter conditions over the course of the 21st century over Austria on an annual basis compared to the reference period 1981–2010 (e.g. RCP4.5 + 107 mm, RCP2.6 + 63 mm for the period 2071–2100). Considering seasonal differences, winter and spring are getting wetter due to an increase in precipitation and a higher fraction of rainfall as a consequence of rising temperatures. In summer only little changes in the mean of the climatic water balance conditions are visible across the model ensemble (e.g. RCP4.5 ±0 mm, RCP2.6 -2 mm for the period 2071–2100). On the contrary, by analysing changes in return periods of drought events, an increasing risk of moderate and extreme drought events during summer is apparent, a signal emerging within the climate system along with increasing warming. [ABSTRACT FROM AUTHOR]

Drought and heat events are becoming more frequent in Europe due to human-induced climate change, affecting many aspects of human well-being and ecosystem functioning. However, the intensity of these drought and heat events is not spatially and temporally uniform. Understanding the spatial variability of drought impacts is important information for decision makers, supporting both planning and preparations to cope with the changing climatic conditions. Currently, data relating to the damage caused by extended drought episodes is scattered across languages and sources such as scientific publications, governmental reports and the media. In this review paper, we compiled data of damages caused by the drought and heat of 2018 until 2022 in forest ecosystems and relate it to large European data sets, providing support for decision making both on the regional and European levels. We partitioned data from 16 European countries to the following regions: Northern, Central, Alpine, and South. We focused on drought and heat damage to forests, and categorized them as (1) physiological (2) pest, and (3) fire damage. We were able to identify the following key trends: (1) Relative defoliation rates of broadleaves is higher than of conifers in every country with the exception of Czech Republic (2) the incidence of wood destroyed by insects is extremely high in Central Europe and Sweden (3) Although forest fires can be related to heat and drought, they are superimposed by other anthropogenic influences (4) In this period (2018–2022), forests in central Europe are particularly affected, while forests in the Northern and Alpine zones are less affected, and adaptations to heat and drought can still be observed in the Southern zone. (5) Although in several regions 2021 was an average year still high levels of damages were observed indicating strong legacy effects of 2018–2020. We note that the inventory should be continuously updated as new data appear. [ABSTRACT FROM AUTHOR]

The 2014-2018 drought over South Africa's winter rainfall zone (WRZ) created a critical water crisis which highlighted the region's drought and climate change vulnerability. Consequently, it is imperative to better understand the climatic characteristics of the drought in order to inform regional adaptation to projected climate change. In this paper we investigate the spatio-temporal patterns of drought intensity and the recent rainfall trends, focusing on assessing the consistency of the prevailing conceptual model of drought drivers with observed patterns. For this we use the new spatial subdivision for the region encompassing the WRZ introduced in our companion paper (Conradie et al., 2022). Compared to previous droughts since 1979, the 2014-2018 drought in the WRZ core was characterised by a markedly lower frequency of very wet days (exceeding the climatological 99.5th percentile daily rainfall -- including dry days) and of wet months (SPI1 > 0:5), a sub-seasonal attribute not previously reported. There was considerable variability in the spatial footprint of the drought. Short-term drought began in the south-western core WRZ in spring 2014. The peak intensity gradually spread north-eastward, although a spatially near-uniform peak is seen during mid-2017. The overall drought intensity for the 2015--2017 period transitions radially from most severe in the WRZ core to least severe in the surroundings. During 2014 and 2015, the drought was most severe at those stations receiving the largest proportion of their rainfall from westerly and north-westerly winds; by 2018, those stations receiving the most rain from the south and south-east were most severely impacted. This indicates an evolving set of dynamic drivers associated with distinct rain-bearing synoptic flows. No evidence is found to support the suggestion that the drought was more severe in the mountain catchments of Cape Town's major supply reservoirs than elsewhere in the core nor that rain day frequency trends since 1979 are more negative in this subdomain. Rainfall and rain day trend rates also exhibit some connections to the spatial seasonality structure of the WRZ, although this is weaker than for drought intensity. Caution should be applied in assessing South African rain day trends given their high sensitivity to observed data shortcomings. Our findings suggest an important role for zonally asymmetric dynamics in the region's drought evolution. This analysis demonstrates the utility of the spatial subdivisions proposed in the companion paper by highlighting spatial structure in drought intensity evolution linked to rainfall dynamics. [ABSTRACT FROM AUTHOR]

Streamflow drought hazard indicators (SDHIs) are mostly lacking in large-scale drought early warning systems (DEWSs). This paper presents a new systematic approach for selecting and computing SDHIs for monitoring drought for human water supply from surface water and for river ecosystems. We recommend considering the habituation of the system at risk (e.g., a drinking water supplier or small-scale farmers in a specific region) to the streamflow regime when selecting indicators; i.e., users of the DEWSs should determine which type of deviation from normal (e.g., a certain interannual variability or a certain relative reduction of streamflow) the risk system of interest has become used to and adapted to. Distinguishing four indicator types, we classify indicators of drought magnitude (water anomaly during a predefined period) and severity (cumulated magnitude since the onset of the drought event) and specify the many relevant decisions that need to be made when computing SDHIs. Using the global hydrological model WaterGAP 2.2d, we quantify eight existing and three new SDHIs globally. For large-scale DEWSs based on the output of hydrological models, we recommend specific SDHIs that are suitable for assessing the drought hazard for (1) river ecosystems, (2) water users without access to large reservoirs, and (3) water users with access to large reservoirs, as well as being suitable for informing reservoir managers. These SDHIs include both drought magnitude and severity indicators that differ by the temporal averaging period and the habituation of the risk system to reduced water availability. Depending on the habituation of the risk system, drought magnitude is best quantified either by the relative deviation from the mean or by the return period of the streamflow value that is based on the frequency of non-exceedance. To compute the return period, we favor empirical percentiles over the standardized streamflow indicator as the former do not entail uncertainties due to the fitting of a probability distribution and can be computed for all streamflow time series. Drought severity should be assessed with indicators that imply habituation to a certain degree of interannual variability, to a certain reduction from mean streamflow, and to the ability to fulfill human water demand and environmental flows. Reservoir managers are best informed by the SDHIs of the grid cell that represents inflow into the reservoir. The DEWSs must provide comprehensive and clear explanations about the suitability of the provided indicators for specific risk systems. [ABSTRACT FROM AUTHOR]

Forecasting the occurrence of hydrological drought according to a forecasting system is an important disaster reduction strategy. In this paper, a new drought prediction model adapted to changing environments was constructed. Taking the Luanhe River basin in China as an example, first, nonstationarity analysis of hydrological sequences in the basin was carried out. Then, conditional distribution models with the human activity factor as an exogenous variable were constructed to forecast hydrological drought based on meteorological drought, and the results were compared with the traditional normal distribution model and conditional distribution model. Finally, a scoring mechanism was applied to evaluate the performance of the three drought forecasting models. The results showed that the runoff series of the Luanhe River basin from 1961 to 2010 were nonstationary; moreover, when human activities were not considered, the hydrological drought class tended to be the same as the meteorological drought class. The calculation results of the models involving HI as an exogenous variable were significantly different from the models that did not consider human activities. When the current drought class tended towards less severe or normal, the meteorological drought tended to turn into more severe hydrological drought with the increase in human index values. According to the scores of the three drought forecasting models, the conditional distribution models involving the human index can further improve the forecasting accuracy of drought in the Luanhe River basin. [ABSTRACT FROM AUTHOR]

In this paper we investigate the spatial patterns and features of meteorological droughts in Europe using concepts and methods derived from complex network theory. Using event synchronization analysis, we uncover robust meteorological drought continental networks based on the co-occurrence of these events at different locations within a season from 1981 to 2020 and compare the results for four accumulation periods of rainfall. Each continental network is then further examined to unveil regional clusters which are characterized in terms of droughts' geographical propagation and source–sink systems. While introducing new methodologies in general climate network reconstruction from raw data, our approach brings out key aspects concerning drought spatial dynamics, which could potentially support droughts' forecast. [ABSTRACT FROM AUTHOR]

2 Special issue statementThis article is part of the special issue "Remote sensing, modelling-based hazard and risk assessment, and management of agro-forested ecosystems". Further, risk assessment has been proven to be a valid tool to evaluate different types of risk both in the riverine (Chen et al., 2019; Lee et al., 2019) and in the forest (Blanco-Gutiérrez et al., 2020) environments: although these environments are different, risk assessment methods play an important role in both. 5 Risk assessment and management for rivers and forests Risk assessment methods can be applied to riverine environments, and river management activities can largely benefit from the use of such methods. 3 Water-related hazards and weather modelling in agriculture Water-related hazards in agriculture are particularly important and are related to variations in water availability in the form of surpluses and shortages of water. [Extracted from the article]

Large-ensemble modelling has become an increasingly popular approach to study the mean climate and the climate system's internal variability in response to external forcing. Here we present the KNMI Large ENsemble TIme Slice (KNMI-LENTIS): a new large ensemble data set produced with the re-turned version of the global climate model EC-Earth3. The ensemble consists of two distinct time slices of 10 year each: a present-day time slice and a +2 K warmer future time slice relative to present-day. The initial conditions for the ensemble members are generated with a combination of micro and macro perturbations. The 10-year length of a single time slice is assumed to be too short to show a significant forced climate change signal, and the ensemble size of 1600 years (160 x 10 years) is assumed to be sufficient to sample the full distribution of climate variability. The time-slice approach makes it possible to study extreme events on sub-annual timescales as well as events that span multiple years such as multi-year droughts and preconditioned compound events. KNMI-LENTIS is therefore uniquely suited to study internal variability and extreme events both at a given climate state and those resulting from forced changes due to external radiative forcing. A unique feature of this ensemble is the high temporal output frequency of the surface water balance and surface energy balance variables, which are stored in 3-hourly intervals, allowing for detailed studies into extreme events. The data set is particularly geared towards research in the land-atmosphere domain. EC-Earth3 has a considerable warm bias in the Southern Ocean and over Antarctica. Hence, users of KNMI-LENTIS are advised to make in-depth comparisons with observational or reanalysis data especially if their studies focus on ocean processes, on locations in the Southern Hemisphere or on teleconnections involving both hemispheres. In this paper, we will give some examples to demonstrate the added value of KNMI-LENTIS for extreme and compound event research and for climate-impact modelling. [ABSTRACT FROM AUTHOR]

The Millennium Drought lasted more than a decade and is notable for causing persistent shifts in the relationship between rainfall and runoff in many southeastern Australian catchments. Research to date has successfully characterised where and when shifts occurred and explored relationships with potential drivers, but a convincing physical explanation for observed changes in catchment behaviour is still lacking. Originating from a large multi-disciplinary workshop, this paper presents and evaluates a range of hypothesised process explanations of flow response to the Millennium Drought. The hypotheses consider climatic forcing, vegetation, soil moisture dynamics, groundwater, and anthropogenic influence. The hypotheses are assessed against evidence both temporally (e.g. why was the Millennium Drought different to previous droughts?) and spatially (e.g. why did rainfall–runoff relationships shift in some catchments but not in others?). Thus, the strength of this work is a large-scale assessment of hydrologic changes and potential drivers. Of 24 hypotheses, 3 are considered plausible, 10 are considered inconsistent with evidence, and 11 are in a category in between, whereby they are plausible yet with reservations (e.g. applicable in some catchments but not others). The results point to the unprecedented length of the drought as the primary climatic driver, paired with interrelated groundwater processes, including declines in groundwater storage, altered recharge associated with vadose zone expansion, and reduced connection between subsurface and surface water processes. Other causes include increased evaporative demand and harvesting of runoff by small private dams. Finally, we discuss the need for long-term field monitoring, particularly targeting internal catchment processes and subsurface dynamics. We recommend continued investment in the understanding of hydrological shifts, particularly given their relevance to water planning under climate variability and change. [ABSTRACT FROM AUTHOR]

Drought is a hydroclimatic extreme that causes perturbations to the terrestrial biosphere and acts as a stressor on vegetation, affecting emissions patterns. During severe drought, isoprene emissions are reduced. In this paper, we focus on capturing this reduction signal by implementing a new percentile isoprene drought stress (yd) algorithm in NASA GISS ModelE based on the MEGAN3 (Model of Emissions of Gases and Aerosols from Nature Version 3) approach as a function of a photosynthetic parameter (Vc,max) and water stress (β). Four global transient simulations from 2003–2013 are used to demonstrate the effect without yd (Default_ModelE) and with online yd (DroughtStress_ModelE). DroughtStress_ModelE is evaluated against the observed isoprene measurements at the Missouri Ozarks AmeriFlux (MOFLUX) site during the 2012 severe drought where improvements in the correlation coefficient indicate it is a suitable drought stress parameterization to capture the reduction signal during severe drought. The application of yd globally leads to a decadal average reduction of ∼2.7 %, which is equivalent to ∼14.6 Tg yr -1 of isoprene. The changes have larger impacts in regions such as the southeastern US. DroughtStress_ModelE is validated using the satellite Ω HCHO column from the Ozone Monitoring Instrument (OMI) and surface O 3 observations across regions of the US to examine the effect of drought on atmospheric composition. It was found that the inclusion of isoprene drought stress reduced the overestimation of Ω HCHO in Default_ModelE during the 2007 and 2011 southeastern US droughts and led to improvements in simulated O 3 during drought periods. We conclude that isoprene drought stress should be tuned on a model-by-model basis because the variables used in the parameterization responses are relative to the land surface model hydrology scheme (LSM) and the effects of yd application could be larger than seen here due to ModelE not having large biases of isoprene during severe drought. [ABSTRACT FROM AUTHOR]

Droughts form a large part of climate- or weather-related disasters reported globally. In Africa, pastoralists living in the arid and semi-arid lands (ASALs) are the worse affected. Prolonged dry spells that cause vegetation stress in these regions have resulted in the loss of income and livelihoods. To curb this, global initiatives like the Paris Agreement and the United Nations recognised the need to establish early warning systems (EWSs) to save lives and livelihoods. Existing EWSs use a combination of satellite earth observation (EO)-based biophysical indicators like the vegetation condition index (VCI) and socio-economic factors to measure and monitor droughts. Most of these EWSs rely on expert knowledge in estimating upcoming drought conditions without using forecast models. Recent research has shown that the use of robust algorithms like auto-regression, Gaussian processes, and artificial neural networks can provide very skilled models for forecasting vegetation condition at short- to medium-range lead times. However, to enable preparedness for early action, forecasts with a longer lead time are needed. In a previous paper, a Gaussian process model and an auto-regression model were used to forecast VCI in pastoral communities in Kenya. The objective of this research was to build on this work by developing an improved model that forecasts vegetation conditions at longer lead times. The premise of this research was that vegetation condition is controlled by factors like precipitation and soil moisture; thus, we used a Bayesian auto-regressive distributed lag (BARDL) modelling approach, which enabled us to include the effects of lagged information from precipitation and soil moisture to improve VCI forecasting. The results showed a ∼2 -week gain in the forecast range compared to the univariate auto-regression model used as a baseline. The R2 scores for the Bayesian ARDL model were 0.94, 0.85, and 0.74, compared to the auto-regression model's R2 of 0.88, 0.77, and 0.65 for 6-, 8-, and 10-week lead time, respectively. [ABSTRACT FROM AUTHOR]

Adaptive policymaking to prepare for current and future drought risks requires an integrated assessment of policy actions and combinations of those under changing conditions. This entails quantification of drought risks, integrating drought probability and socio-economic consequences for all relevant sectors that are potentially impacted by drought. The investment costs of proposed policy actions and strategies (various actions combined) can then be compared with the expected risk reduction to determine the cost-effectiveness. This paper presents a method to quantify drought risk in the Netherlands under changing future conditions and in response to policy actions. It illustrates how to use this information as part of a societal cost–benefit analysis and in building an adaptive long-term strategy. The method has been successfully applied to support decision making on the Netherlands' national drought risk management strategy as part of the National Delta Programme for climate change adaptation. [ABSTRACT FROM AUTHOR]

Global-scale river routing models (RRMs) are commonly used in a variety of studies, including studies on the impact of climate change on extreme flows (floods and droughts), water resources monitoring or large-scale flood forecasting. Over the last two decades, the increasing number of observational datasets, mainly from satellite missions, and increasing computing capacities have allowed better performance by RRMs, namely by increasing their spatial resolution. The spatial resolution of a RRM corresponds to the spatial resolution of its river network, which provides the flow directions of all grid cells. River networks may be derived at various spatial resolutions by upscaling high-resolution hydrography data. This paper presents a new global-scale river network at 1/12 ∘ derived from the MERIT-Hydro dataset. The river network is generated automatically using an adaptation of the hierarchical dominant river tracing (DRT) algorithm, and its quality is assessed over the 70 largest basins of the world. Although this new river network may be used for a variety of hydrology-related studies, it is provided here with a set of hydro-geomorphological parameters at the same spatial resolution. These parameters are derived during the generation of the river network and are based on the same high-resolution dataset, so that the consistency between the river network and the parameters is ensured. The set of parameters includes a description of river stretches (length, slope, width, roughness, bankfull depth), floodplains (roughness, sub-grid topography) and aquifers (transmissivity, porosity, sub-grid topography). The new river network and parameters are assessed by comparing the performances of two global-scale simulations with the CTRIP model, one with the current spatial resolution (1/2 ∘) and the other with the new spatial resolution (1/12 ∘). It is shown that, overall, CTRIP at 1/12 ∘ outperforms CTRIP at 1/2 ∘ , demonstrating the added value of the spatial resolution increase. The new river network and the consistent hydro-geomorphology parameters, freely available for download from Zenodo (10.5281/zenodo.6482906,), may be useful for the scientific community, especially for hydrology and hydro-geology modelling, water resources monitoring or climate studies. [ABSTRACT FROM AUTHOR]

This paper describes the benefits of using more reliable local climate scenarios to analyse hydrological responses. It assumes that Regional Climate Model (RCM) simulations are more reliable when they provide better approximations to the historical basic and drought statistics after applying a bias correction to them. We have investigated whether the best solutions in terms of their approximation to the local meteorology may also provide the best hydrological assessments. We have carried out a classification of the corrected RCM simulations used for both approximations. This has been applied in the Cenajo basin (south-eastern Spain), where we show that the best approximations of the historical meteorological statistics also provide the best approximations for the hydrological statistics. The selected RCMs were used to generate future (2071–2100) local scenarios under the RCP8.5 emission scenario. The two selected RCMs predict significant changes in mean precipitation (-31.6 % and -44.0 %) and mean temperature (+26.0 % and +32.2 %). They also predict higher frequency (from 5 events in the historical period to 20 and 22 in the future), length (4.8 to 7.4 and 10.5 months), magnitude (2.53 to 6.56 and 9.62 SPI) and intensity (0.48 to 1.00 and 0.94 SPI) of extreme meteorological droughts. These two RCMs also predict higher changes in mean streamflow (-43.5 % and -57.2 %) and hydrological droughts. The two RCMs also predict worrying changes in streamflow (-43.5 % and -57.2 %) and hydrologically extreme droughts: frequency (from 3 to 11 for the first model and 8 events for the second model), length (8.3 to 15.4 and 29.6 months), magnitude (from 3.98 to 11.84 and 31.72 SSI), and intensity (0.63 to 0.90 and 1.52 SSI). [ABSTRACT FROM AUTHOR]

Soil moisture (SM) plays a significant role in many natural and anthropogenic systems which are essential to supporting life on Earth. Thus, accurate measurement and assessment of changes in soil moisture globally is of great value, including long-term historical assessment. Since the on-board cycle and detailed parameters of disparate sensors are different, the European Space Agency established the Climate Change Initiative (CCI) program to harmonize the available multisource SM data, producing long time-series surface SM datasets starting from 1978 to the present. However, the Soil Moisture Active Passive (SMAP) mission, launched in 2015, has shown more satisfactory performance in both spatial accuracy and in capturing pattern of temporal changes. In this paper, a random forest (RF) model was proposed to extend the superior SMAP dataset historically (named RF_SMAP), using the corresponding CCI data time-series. We assumed that the temporal changes in the SMAP dataset are similar generally to those in the available CCI dataset. Accordingly, the RF model was constructed using the temporal characteristics extracted from the CCI SM v05.2 data (coupled with three terrain characteristics and two location characteristics), which was migrated to the prediction of the RF_SMAP dataset. The available in-situ SM data and the real SMAP data from April 2015 to April 2016 were used as references to validate the predicted RF_SMAP data. It was shown that compared with the CCI dataset, the predicted RF_SMAP dataset is closer to the in-situ SM data and the real SMAP data. Moreover, the historical RF_SMAP dataset is more accurate than the widely used Global Land Evaporation Amsterdam Model (GLEAM) dataset in terms of average root mean square error (RMSE), bias (Bias), and Kling-Gutpa efficiency (KGE). Thus, the RF_SMAP dataset was shown to be a reliable substitute for the historical CCI dataset, with an unbiased root mean square error (ubRMSE) of 0.035. The new long time-series RF_SMAP dataset, which will be available to download, will be of great value for a range of research in applications such as climate assessment, agricultural planning, food insecurity monitoring and drought assessment and monitoring. [ABSTRACT FROM AUTHOR]

Drought events have been linked with the enhancements of organic aerosols (OAs), but the mechanisms have not been comprehensively understood. This study investigates the relationships between the monthly standardized precipitation–evapotranspiration index (SPEI) and surface OA in the contiguous United States (CONUS) during the summertime from 1998 to 2018. OA under severe drought conditions shows a significant increase in mass concentrations across most of the CONUS relative to non-drought periods, with the Pacific Northwest (PNW) and Southeast United States (SEUS) experiencing the highest average enhancement of 1.79 µgm-3 (112 %) and 0.92 µgm-3 (33 %), respectively. In the SEUS, a linear regression approach between OA and sulfate was used to estimate the epoxydiol-derived secondary organic aerosol (IEPOX SOA), which is the primary driver of the OA enhancements under droughts due to the simultaneous increase in emissions of biogenic volatile organic compounds (VOCs; such as isoprene and monoterpene) and sulfate. The rise in sulfate is mainly caused by the reduced wet deposition because of the up to 62 % lower precipitation amount. In the PNW, OA enhancements are closely linked to intensified wildfire emissions, which raise OA mass concentrations to be 4–8 times higher relative to non-fire conditions. All 10 Earth system models participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) can capture the slopes between SPEI and OA in the PNW, with CESM2-WACCM and GFDL-ESM4 performing the best and worst in predicting the OA enhancement under severe droughts. However, all models significantly underestimate the OA increase in the SEUS, with Nor-ESM2-LM and MIRCO6 showing relatively better performance. This study reveals the key drivers of the elevated OA levels under droughts in the CONUS and underscores the deficiencies of current climate models in their predictive capacity for assessing the impact of future droughts on air quality. [ABSTRACT FROM AUTHOR]

Streamflow drought hazard indicators (SDHI) are mostly lacking in large-scale drought early warning systems (DEWS). This paper presents a new systematic approach for selecting and computing SDHI for monitoring drought risk for human water supply from surface water and for river ecosystems that is also relevant for meteorological or soil moisture drought. We recommend considering the habituation of people and ecosystems to the streamflow regime (e.g., a certain interannual variability or relative reduction of streamflow) when selecting indicators. Distinguishing four indicator types, we classify indicators of drought magnitude (water deficit during a pre-defined period) and severity (cumulated magnitude since onset of the drought event). We quantify eight existing and three new SDHI globally using the global hydrological model WaterGAP2.2d. We recommend streamflow hazard indicators that should be included in large-scale DEWS as they are suitable for risk systems that are differently adapted to low water availability and characterized by either perennial or intermittent streamflow regime and the existence or not of large reservoirs. Drought magnitude is best quantified by return period or relative deviation from mean, and severity by return period or water volume below a threshold relative to mean annual streamflow. Both anomaly and deficit indicators should be provided. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) plays a significant role in many natural and anthropogenic systems which are essential to supporting life on Earth. Thus, accurate measurement and assessment of changes in soil moisture globally is of great value, including long-term historical assessment. Since the on-board cycle and detailed parameters of disparate sensors are different, the European Space Agency established the Climate Change Initiative (CCI) program to harmonize the available multisource SM data, producing long time-series surface SM datasets starting from 1978 to the present. However, the Soil Moisture Active Passive (SMAP) mission, launched in 2015, has shown more satisfactory performance in both spatial accuracy and in capturing pattern of temporal changes. In this paper, a random forest (RF) model was proposed to extend the superior SMAP dataset historically (named RF_SMAP), using the corresponding CCI time-series. We assumed that the temporal changes in the SMAP dataset are similar generally to those in the available CCI dataset. Accordingly, the RF model was constructed using the CCI SM v05.2 data, which was migrated to the prediction of the RF_SMAP dataset. The available in-situ SM data and the real SMAP data from 2015 to 2019 were used as references to validate the predicted RF_SMAP data. It was shown that compared with the CCI dataset, the predicted RF_SMAP dataset is closer to the in-situ SM data and the real SMAP data. Thus, the RF_SMAP dataset was shown to be a reliable substitute for the historical CCI dataset. The new long time-series RF_SMAP dataset, which will be available to download, will be of great value for a range of research in applications such as climate assessment, agricultural planning, food insecurity monitoring and drought assessment and monitoring. [ABSTRACT FROM AUTHOR]

In this paper future changes of surface water availability in Austria are investigated. We use an ensemble of downscaled and bias-corrected regional climate model simulations of the EURO-CORDEX initiative under moderate mitigation (RCP4.5) and Paris agreement (RCP2.6) emission scenarios. The climatic water balance and its components (rainfall, snow melt, glacier melt and atmospheric evaporative demand) are used as indicators for surface water availability and we focus on different altitudinal classes (lowland, mountainous and high alpine) to depict a variety of processes in complex terrain. Apart from analysing the mean changes of these components we also pursue a hazard risk approach by estimating future changes in return periods of drought events of a given magnitude as observed in the reference period. The results show in general wetter conditions over the course of the 21st century over Austria on an annual basis compared to the reference period 1981-2010. Considering seasonal differences, winter and spring are getting wetter due to an increase in precipitation and a higher fraction of rainfall as a consequence of rising temperatures. In summer only little changes in the mean of the climatic water balance conditions are visible across the model ensemble. On the contrary, by analysing changes in return periods of drought events, an increasing risk of moderate and extreme drought events during summer is apparent, a signal emerging within the climate system along increasing warming. [ABSTRACT FROM AUTHOR]

Adaptive policy-making to prepare for current and future drought risks requires an integrated assessment of policy actions and combinations of those under changing conditions. This entails quantification of drought risks, integrating drought probability and socio-economic consequences for all relevant sectors that are potentially impacted by drought. The investment costs of proposed policy actions and strategies (various actions combined) can then be compared with the expected risk reduction to determine the cost-effectiveness. This paper presents a method to quantify drought risk in the Netherlands under changing future conditions and in response to policy actions. It illustrates how to use this information as part of a societal cost-benefit analysis and in building an adaptive long-term strategy. The method has been successfully applied to support decision making on the Netherlands' national drought risk management strategy as part of the National Delta Program for climate change adaptation. [ABSTRACT FROM AUTHOR]

Drought stress causes multiple feedback responses in plants. These responses span from stomata closure and enzymatic downregulation of photosynthetic activity to structural adjustments of xylem biomass and leaf area. Some of these processes are not easily reversible and may persist long after the stress has ended. Despite a multitude of hydraulic model approaches, simulation models still widely lack an integrative mechanistic description of how this sequence of physiological to structural tree responses may be realized that is also simple enough to be generally applicable. Here, we suggest an integrative, sequential approach to simulate drought stress responses. First, decreasing plant water potential triggers stomatal closure alongside a downregulation of photosynthetic performance, thereby effectively slowing down further desiccation. A second protective mechanism is introduced by increasing the soil–root resistance, represented by a disconnection of fine roots after a threshold soil water potential has been reached. Further decreases in plant water potential due to residual transpiration and loss of internal stem water storage consistently lead to a loss of hydraulic functioning, which is reflected in sapwood loss and foliage senescence. This new model functionality has been used to investigate the responses of tree hydraulics, carbon uptake, and transpiration to soil and atmospheric drought in an extremely dry Aleppo pine (Pinus halepensis Mill.) plantation. Using the hypothesis of a sequential triggering of stress-mitigating responses, the model was able to reflect carbon uptake and transpiration patterns under varying soil water supply and atmospheric demand conditions – especially during summer – and respond realistically regarding medium-term responses, such as leaf and sapwood senescence. We could show that the observed avoidance strategy was only achieved when the model accounted for very early photosynthesis downregulation, and the relatively high measured plant water potentials were well reproduced with a root–soil disconnection strategy that started before major xylem conductance losses occurred. Residual canopy conductance was found to be pivotal in explaining dehydration and transpiration patterns during summer, but it also disclosed the fact that explaining the water balance in the driest periods requires water supply from stem water and deep soil layers. In agreement with the high drought resistance observed at the site, our model indicated little loss of hydraulic functioning in Aleppo pine, despite the intensive seasonal summer drought. [ABSTRACT FROM AUTHOR]

The impact of drought on environmental flow (EF) in 27 catchments of the Indus Basin is studied from 1980–2018 using indicators of hydrologic alterations (IHAs). The standardized precipitation evapotranspiration index (SPEI) was systematically propagated from one catchment to another using principal component analysis (PCA). Threshold regression is used to determine the severity of drought (scenario 1, drought severity that causes low flows) and the month (scenario 2, months where drought has resulted in low flows) that trigger low flows in the Indus Basin. The impact of drought on low EFs is quantified using range of variability analysis (RVA), which is an integrated component of the IHA used to study the hydrological alterations in environmental flow components (EFCs) by comparing the pre- and post-impact periods of human and/or climate interventions in EFCs. The hydrological alteration factor (HAF) is calculated for each catchment in the Indus Basin. The results show that most of the catchments were vulnerable to drought during the periods of 1984 to 1986, 1991/1992, 1997 to 2003, 2007 to 2008, 2012 to 2013, and 2017 to 2018. On a longer timescale (SPEI-12), drought is more severe in the lower Indus Basin (LIB) than in the upper Indus Basin (UIB). The IHA pointed out that drought significantly impacts the distribution of EFCs, particularly extremely low flow (ELF) and low flow (LF). The magnitude and frequency of the ELF and LF events increase as drought severity increases. The threshold regression provided useful insights, indicating that moderate drought can trigger ELF and LF at shorter timescales (SPEI-1 and SPEI-6) in the UIB and middle Indus Basin (MIB). Conversely, severe and extreme droughts trigger ELF and LF at longer timescales (SPEI-12) in the LIB. The threshold regression also divided the entire study period (1980–2018) into different time periods (scenario 2), which is useful for quantifying the impact of drought on low EFs using the SPEI coefficient. Higher SPEI coefficients are observed in the LIB, indicating high alterations in EF due to drought. HAF showed high alterations in EF in most of the catchments throughout the year except in August and September. Overall, this study provided useful insights for analysing the effects of drought on EF, especially during low flows. [ABSTRACT FROM AUTHOR]

The co-occurrence of meteorological, agricultural, and hydrological droughts (multivariate compound droughts) in Switzerland during growing season is problematic due to limitations in water abstractions from rivers during low-flow periods, while at the same time the need for irrigation is high. We analyse compound droughts for 52 catchments in Switzerland during the extended summer season (May–October) using the transient climate and hydrological scenarios for Switzerland (CH2018 and Hydro-CH2018) for both a scenario with mitigation (representative concentration pathway 2.6 (RCP2.6), 8 model chains) and a scenario without mitigation (RCP8.5, 20 model chains). In the RCP8.5 scenario the number of compound drought days is projected to significantly increase by mid-century across all greater regions of Switzerland. The increased frequency is mainly a result of more frequent events (significant) rather than longer event durations (non-significant). Models generally agree on the sign of change. By 2085, compound drought events are projected to occur in median once per catchment per extended summer season north of the Alps and every 1–2 years south of the Alps. Further, the increases in compound drought days mainly occur between May–October, leading to a shift in the main agricultural production season and a more pronounced seasonality with the highest occurrence probabilities between mid-July and the beginning of October. Coupled to the increase in days and events, significantly more catchments are projected to be affected by compound droughts at the same time. In the RCP2.6 (mitigation) scenario, the increase in the number of compound drought days and events is not significant by the end of the 21st century. In comparison with RCP8.5, the number of compound drought days is reduced by 50 %–55 % north of the Alps and by up to 75 % south of the Alps by the end of the century. This emphasizes the need for coordinated adaptation in combination with mitigation measures taken at an early stage. [ABSTRACT FROM AUTHOR]

Since 2010, central Chile has experienced a protracted megadrought with annual precipitation deficits ranging from 25 % to 70 %. An intensification of drought propagation has been attributed to the effect of cumulative precipitation deficits linked to catchment memory. Yet, the influence of water extractions on drought intensification is still unclear. Our study assesses climate and water use effects on streamflow reductions during a high-human-influence period (1988–2020) in four major agricultural basins. We performed this attribution by contrasting observed streamflow (driven by climate and water use) with near-natural streamflow simulations (driven mainly by climate) representing what would have occurred without water extractions. Near-natural streamflow estimations were obtained from rainfall–runoff models trained over a reference period with low human intervention (1960–1988). Annual and seasonal streamflow reductions were examined before and after the megadrought onset, and hydrological drought events were characterized for the complete evaluation period in terms of their frequency, duration, and intensity. Our results show that before the megadrought onset (1988–2009) the mean annual deficits in observed streamflow ranged between 2 % and 20 % across the study basins and that 81 % to 100 % of those deficits were explained by water extractions. During the megadrought (2010–2020), the mean annual deficits in observed streamflow were 47 % to 76 % among the basins. During this time, the relative contribution of precipitation deficits on streamflow reduction increased while the contribution of water extractions decreased, accounting for 27 % to 51 % of the streamflow reduction. Regarding drought events during the complete evaluation period, we show that human activities have amplified drought propagation, with almost double the intensity of hydrological droughts in some basins compared to those expected by precipitation deficits only. We conclude that while the primary cause of streamflow reductions during the megadrought has been the lack of precipitation, water uses have not diminished during this time, causing an exacerbation of the hydrological drought conditions and aggravating their impacts on water accessibility in rural communities and natural ecosystems. [ABSTRACT FROM AUTHOR]

Drought has long posed an existential threat to society. Engineering and technological advancements have enabled the development of complex, interconnected water supply systems that buffer societies from the impacts of drought, enabling growth and prosperity. However, increasing water demand from population growth and economic development, combined with more extreme and prolonged droughts due to climate change, poses significant challenges for governments in the 21st century. Improved understanding of the cascading multisectoral impacts and adaptive responses resulting from extreme drought can aid in adaptive planning and highlight key processes in modeling drought impacts. The record drought spanning 2008 to 2015 in the Colorado Basin in the state of Texas, United States, serves as an outstanding illustration to assess multisectoral impacts and responses to severe, multi-year drought. The basin faces similar water security challenges to those across the western US, such as groundwater depletion and sustainability, resource competition between agriculture and growing urban populations, limited options for additional reservoir expansion, and the heightened risk of more severe and frequent droughts due to climate change. By analyzing rich, high-quality data sourced from nine different local, state, and federal sources, we demonstrate that characterizing regional multisector dynamics is crucial to predicting and understanding future vulnerability and possible approaches to reduce impacts to human and natural systems in the face of extreme drought conditions. This review reveals that, despite the severe hydrometeorological conditions of the drought, the region's advanced economy and existing water infrastructure effectively mitigated economic and societal impacts. [ABSTRACT FROM AUTHOR]

Droughts often lead to cross-sectoral and interconnected socio-economic impacts, affecting human well-being, ecosystems, and economic development. Extended drought periods, such as the 2018–2022 event in Germany, amplify these impacts due to temporal carry-over effects. Yet, our understanding of drought impact dynamics during increasingly frequent multi-year drought periods is still in its infancy. In this study, we analyse the socio-economic impacts of the 2018–2022 multi-year drought in Germany and compare them to previous single-year events. Leveraging text-mining tools, we derive a dataset covering impacts reported by 260 news outlets on agriculture, forestry, livestock, waterways, aquaculture, fire, and social impacts spanning 2000 to 2022. We introduce the concept of drought impact profiles (DIPs) to describe spatio-temporal patterns of the reported co-occurrences of impacts. We employ a clustering algorithm to detect these DIPs and then use sequence mining and statistical tests to analyse spatio-temporal trends. Our results reveal that the 2018–2022 multi-year drought event had distinct impact patterns compared to prior single-year droughts regarding their spatial extent, impact diversity, and prevalent impact types. For the multi-year drought period, we identify shifts in how impacts have been perceived regionally, especially focusing on legacy and cascading effects on forestry and social activities. Also, we show how regional differences in relevant impacts are controlled by different land-cover types. Our findings enhance the understanding of the dynamic nature of drought impacts, highlighting the potential of text-mining techniques to study drought impact dynamics. The insights gained underscore the need for different strategies in managing multi-year droughts compared to single-year events. [ABSTRACT FROM AUTHOR]

The 2015–2016 Amazon drought was characterized by below-average regional precipitation for an entire year, which distinguishes it from the dry-season-only droughts in 2005 and 2010. Studies of vegetation indices (VIs) derived from optical remote sensing over the Amazonian forests indicated three stages in canopy response during the 2015–2016 drought, with below-average greenness during the onset and end of the drought, and above-average greenness during the intervening months. To date, a satisfactory explanation for this broad temporal pattern has not been found. A better understanding of rainforest behaviors during this unusually long drought should help predict their response to future droughts. We hypothesized that negative VI anomalies could be caused by water and heat stress exceeding the tolerance ranges of the rainforest. To test our hypothesis, based on monthly observations of terrestrial water storage (TWS), land surface temperature (LST), and vapor pressure deficit (VPD) for January 2003 to December 2016, we proposed an approach to categorize regions into two groups: (1) those exceeding normal hydrological and thermal ranges and (2) those within normal ranges. Accordingly, regions exceeding normal ranges during different stages of the 2015–2016 event were delineated. The results showed a gradual southward shift in these regions: from the northeastern Amazon during August to October 2015 to the north–central part during November 2015 to February 2016 and finally to the southern Amazon in July 2016. Over these regions exceeding normal ranges during droughts, negative VI anomalies were expected, irrespective of radiation anomalies. Over the regions within normal ranges, VI anomalies were assumed to respond positively to radiation anomalies, as is expected under normal conditions. We found that our proposed approach can explain more than 70 % of the observed spatiotemporal patterns in VI anomalies during the 2015–2016 drought. These results suggest that our "exceeding normal ranges"-based approach combining (i) water storage, (ii) temperature, and (iii) atmospheric moisture demand drivers can reasonably identify the most likely drought-affected regions at monthly to seasonal timescales. Using observation-based hydrological and thermal condition thresholds can help with interpreting the response of the Amazon rainforest to future drought events. [ABSTRACT FROM AUTHOR]

There are indications that the reference climatology underlying meteorological drought has shown nonstationarity at seasonal, decadal, and centennial timescales, impacting the calculation of drought indices and potentially having ecological and economic consequences. Analyzing these trends in meteorological drought climatology beyond 100 years, a time frame which exceeds the available period of observation data, contributes to a better understanding of the nonstationary changes, ultimately determining whether they are within the range of natural variability or outside this range. To accomplish this, our study introduces a novel approach to integrate unevenly scaled tree-ring proxy data from the North American Seasonal Precipitation Atlas (NASPA) with instrumental precipitation datasets by first temporally downscaling the proxy data to produce a regular time series and then modeling climate nonstationarity while simultaneously correcting model-induced bias. This new modeling approach was applied to 14 sites across the continental United States using the 3-month standardized precipitation index (SPI) as a basis. The findings showed that certain locations have experienced recent rapid shifts towards drier or wetter conditions during the instrumental period compared to the past 1000 years, with drying trends generally found in the west and wetting trends in the east. This study also found that seasonal shifts have occurred in some regions recently, with seasonality changes most notable for southern gauges. We expect that our new approach provides a foundation for incorporating various datasets to examine nonstationary variability in long-term precipitation climatology and to confirm the spatial patterns noted here in greater detail. [ABSTRACT FROM AUTHOR]

Compound drought and heatwave (CDHW) events can result in intensified damage to ecosystems, economies, and societies, especially on a warming planet. Although it has been reported that CDHW events in the winter season can also affect insects, birds, and the occurrence of wildfires, the literature generally focuses exclusively on the summer season. Moreover, the coarse temporal resolution of droughts as determined on a monthly scale may hamper the precise identification of the start and/or end dates of CDHW events. Therefore, we propose a method to identify CDHW events on a daily scale that is applicable across the four seasons. More specifically, we use standardized indices calculated on a daily scale to identify four types of compound events in a systematic way. Based on the hypothesis that droughts or heatwaves should be statistically extreme and independent, we remove minor dry or warm spells and merge mutually dependent ones. To demonstrate our method, we make use of 120 years of daily precipitation and temperature information observed at Uccle, Brussels-Capital Region, Belgium. Our method yields more precise start and end dates for droughts and heatwaves than those that can be obtained with a classical approach acting on a monthly scale, thereby allowing for a better identification of CDHW events. Consistent with existing literature, we find an increase in the number of days in CDHW events at Uccle, mainly due to the increasing frequency of heatwaves. Our results also reveal a seasonality in CDHW events, as droughts and heatwaves are negatively dependent on one another in the winter season at Uccle, whereas they are positively dependent on one another in the other seasons. Overall, the method proposed in this study is shown to be robust and displays potential for exploring how year-round CDHW events influence ecosystems. [ABSTRACT FROM AUTHOR]

In recent years, extreme droughts in the United States have increased in frequency and severity, underlining a need to improve our understanding of vegetation resilience and adaptation. Flash droughts are extreme events marked by the rapid dry down of soils due to lack of precipitation, high temperatures, and dry air. These events are also associated with reduced preparation, response, and management time windows before and during drought, exacerbating their detrimental impacts on people and food systems. Improvements in actionable information for flash drought management are informed by atmospheric and land surface processes, including responses and feedbacks from vegetation. Phenologic state, or growth stage, is an important metric for modeling how vegetation modulates land–atmosphere interactions. Reduced stomatal conductance during drought leads to cascading effects on carbon and water fluxes. We investigate how uncertainty in vegetation phenology and stomatal regulation propagates through vegetation responses during drought and non-drought periods by coupling a land surface hydrology model to a predictive phenology model. We assess the role of vegetation in the partitioning of carbon, water, and energy fluxes during flash drought and carry out a comparison against drought and non-drought periods. We selected study sites in Kansas, USA, that were impacted by the flash drought of 2012 and that have AmeriFlux eddy covariance towers which provide ground observations to compare against model estimates. Results show that the compounding effects of reduced precipitation and high vapor pressure deficit (VPD) on vegetation distinguish flash drought from other drought and non-drought periods. High VPD during flash drought shuts down modeled stomatal conductance, resulting in rates of evapotranspiration (ET), gross primary productivity (GPP), and water use efficiency (WUE) that fall below those of average drought conditions. Model estimates of GPP and ET during flash drought decrease to rates similar to what is observed during the winter, indicating that plant function during drought periods is similar to that of dormant months. These results have implications for improving predictions of drought impacts on vegetation. [ABSTRACT FROM AUTHOR]

Drought represents a prevalent climate risk in the Mediterranean region. In the context of climate change, an increase in both frequency and intensity is anticipated over the next century. In order to effectively manage future scenarios where global warming overlays natural climate variability, a thorough analysis of the nature of droughts prior to the industrial age is imperative. This approach incorporates an extended temporal scale into the study of severe droughts, enabling the identification of low-frequency drought events that occurred before the instrumental period. The objective of this study is to examine the occurrence and magnitude of extreme droughts lasting over a year in the Spanish Mediterranean Basin during the Early 19th Century (1790–1830). To achieve this objective, the research integrates the use of instrumental observations and information derived from historical documentary sources with daily to monthly resolutions (e.g. rogation ceremonies). The findings reveal that drought episodes were more frequent and severe during the Early 19th Century than in the second half of this century. Moreover, drought episodes of similar severity were rare throughout the 20th Century. Only in the current context of climate change, over the last two decades, has a pattern of high drought severity been identified that resembles the severity found during the Early 19th Century (especially between 1812 and 1825). This study underscores the presence of high variability in drought patterns over the last centuries, justifying the need for intensified research on drought episodes with high temporal resolution for extended periods. [ABSTRACT FROM AUTHOR]

Satellite-derived agricultural drought indices can provide a complementary perspective of terrestrial vegetation trends. In addition, their integration for drought assessments under future climates is beneficial for providing more comprehensive assessments. However, satellite-derived drought indices are only available for the Earth observation era. In this study, we aim to improve the agricultural drought assessments under future climate change by applying deep learning (DL) to predict satellite-derived vegetation indices from a regional climate simulation. The simulation is produced by the Terrestrial Systems Modeling Platform (TSMP) and performed in a free evolution mode over Europe. TSMP simulations incorporate variables from underground to the top of the atmosphere (ground-to-atmosphere; G2A) and are widely used for research studies related to water cycle and climate change. We leverage these simulations for long-term forecasting and DL to map the forecast variables into normalized difference vegetation index (NDVI) and brightness temperature (BT) images that are not part of the simulation model. These predicted images are then used to derive different vegetation and agricultural drought indices, namely NDVI anomaly, BT anomaly, vegetation condition index (VCI), thermal condition index (TCI), and vegetation health index (VHI). The developed DL model could be integrated with data assimilation and used for downstream tasks, i.e., for estimating the NDVI and BT for periods where no satellite data are available and for modeling the impact of extreme events on vegetation responses with different climate change scenarios. Moreover, our study could be used as a complementary evaluation framework for TSMP-based climate change simulations. To ensure reliability and to assess the model's applicability to different seasons and regions, we provide an analysis of model biases and uncertainties across different regions over the pan-European domain. We further provide an analysis about the contribution of the input variables from the TSMP model components to ensure a better understanding of the model prediction. A comprehensive evaluation of the long-term TSMP simulation using reference remote sensing data showed sufficiently good agreements between the model predictions and observations. While model performance varies on the test set between different climate regions, it achieves a mean absolute error (MAE) of 0.027 and 1.90 K with coefficient of determination (R2) scores of 0.88 and 0.92 for the NDVI and BT, respectively, at 0.11° resolution for sub-seasonal predictions. In summary, we demonstrate the feasibility of using DL on a TSMP simulation to synthesize NDVI and BT satellite images, which can be used for agricultural drought forecasting. Our implementation is publicly available at the project page (https://hakamshams.github.io/Focal-TSMP , last access: 4 April 2024). [ABSTRACT FROM AUTHOR]

About 80 % of the precipitation at the Colorado River's headwaters is snow, and the resulting snowmelt-driven hydrograph is a crucial water source for about 40 million people. Snowmelt from alpine and subalpine snowpack contributes substantially to groundwater recharge and river flow. However, the dynamics of snowmelt progression are not well understood because observations of the high-elevation snowpack are difficult due to challenging access in complex mountainous terrain as well as the cost and labor intensity of currently available methods. We present a novel approach to infer the processes and dynamics of high-elevation snowmelt contributions predicated upon stable hydrogen and oxygen isotope ratios observed in streamflow. We show that deuterium-excess (d-excess) values of stream water could serve as a comparatively cost-effective proxy for a catchment-integrated signal of high-elevation snowmelt contributions to catchment runoff. We sampled stable hydrogen and oxygen isotope ratios of the precipitation, snowpack, and stream water in the East River, a headwater catchment of the Colorado River, and the stream water of larger catchments at sites on the Gunnison River and Colorado River. The d-excess of snowpack increased with elevation; the upper subalpine and alpine snowpack (> 3200 m) had substantially higher d-excess compared to lower elevations (< 3200 m) in the study area. The d-excess values of stream water reflected this because d-excess values increased as the higher-elevation snowpack contributed more to stream water generation later in the snowmelt/runoff season. End-member mixing analyses based on the d-excess data showed that the share of high-elevation snowmelt contributions within the snowmelt hydrograph was on average 44 % and generally increased during melt period progression, up to 70 %. The observed pattern was consistent during 6 years for the East River, and a similar relation was found for the larger catchments on the Gunnison and Colorado rivers. High-elevation snowpack contributions were found to be higher for years with lower snowpack and warmer spring temperatures. Thus, we conclude that the d-excess of stream water is a viable proxy to observe changes in high-elevation snowmelt contributions in catchments at various scales. Inter-catchment comparisons and temporal trends of the d-excess of stream water could therefore serve as a catchment-integrated measure to monitor if mountain systems rely on high-elevation water inputs more during snow drought compared to years of average snowpack depths. [ABSTRACT FROM AUTHOR]

Global scale river routing models (RRMs) are commonly used in a variety of studies, including studies on the impact of climate change on extreme flows (floods and droughts), water resources monitoring or large scale flood forecasting. Over the last two decades, the increasing number of observational datasets, mainly from satellite missions, and the increasing computing capacities, have allowed better performances of RRMs, namely by increasing their spatial resolution. The spatial resolution of a RRM corresponds to the spatial resolution of its river network, which provides flow direction of all grid cells. River networks may be derived at various spatial resolution by upscaling high resolution hydrography data. This paper presents a new global scale river network at 1/12° derived from the MERIT-Hydro dataset. The river network is generated automatically using an adaptation of the Hierarchical Dominant River Tracing (DRT) algorithm, and its quality is assessed over the 70 largest basins of the world. Although this new river network may be used for a variety of hydrology-related studies, it is here provided with a set of hydro-geomorphological parameters at the same spatial resolution. These parameters are derived during the generation of the river network and are based on the same high resolution dataset, so that the consistency between the river network and the parameters is ensured. The set of parameters includes a description of river stretches (length, slope, width, roughness, bankfull depth), floodplains (roughness, sub-grid topography) and aquifers (transmissivity, porosity, sub-grid topography). The new river network and parameters are assessed by comparing the performances of two global scale simulations with the CTRIP model, one with the current spatial resolution (1/2°) and the other with the new spatial resolution (1/12°). It is shown that CTRIP at 1/12° overall outperforms CTRIP at 1/2°, demonstrating the added value of the spatial resolution increase. The new river network and the consistent hydro-geomorphology parameters may be useful for the scientific community, especially for hydrology and hydro-geology modelling, water resources monitoring or climate studies. [ABSTRACT FROM AUTHOR]

Human-caused climate change increases the occurrence and severity of droughts due to increasing temperatures, altered circulation patterns and reduced snow occurrence. For example, Europe has suffered from drought events in the last decade like never since the beginning of weather recording. Here we present soil moisture data from 65 Cosmic-ray neutron sensors (CRNS) in Europe (COSMOS-Europe for short) covering recent drought events. The CRNS sites are distributed across Europe and cover all major land use types and climate zones in Europe. The raw neutron count data from the CRNS stations were provided by 23 research institutions and processed using state-of-the-art methods. The harmonised processing included correction of the raw neutron counts, and a harmonised methodology for the conversion into soil moisture based on available in-situ information. In addition, information on the data uncertainty is provided with the dataset, information that is particularly useful for remote sensing and modelling applications. This paper presents the current spatiotemporal coverage of CRNS stations in Europe and describes the protocols for data processing from raw measurements to consistent soil moisture products as well as first results on how the recent drought events have been captured by the CRNS network. This harmonised European soil moisture dataset will help both hydrologists and climate scientists to study individual drought events, to understand their causes, to evaluate and improve their modelling, and to estimate the extremity of current events. The dataset, entitled “Dataset of COSMOS-Europe: A European network of Cosmic-Ray Neutron Soil Moisture Sensors”, is shared via Forschungszentrum Jülich: https://doi.org/10.34731/x9s3-kr48 (Bogena and Ney, 2021). [ABSTRACT FROM AUTHOR]

There is a wide variety of drought indices, yet a consensus on suitable indices and temporal scales for monitoring streamflow drought remains elusive across diverse hydrological settings. Considering the growing interest in spatially distributed indices for ungauged areas, this study addresses the following questions: (i) What temporal scales of precipitation-based indices are most suitable to assess streamflow drought in catchments with different hydrological regimes? (ii) Do soil moisture indices outperform meteorological indices as proxies for streamflow drought? (iii) Are snow indices more effective than meteorological indices for assessing streamflow drought in snow-influenced catchments? To answer these questions, we examined 100 near-natural catchments in Chile with four hydrological regimes, using the standardised precipitation index (SPI), standardised precipitation evapotranspiration index (SPEI), empirical standardised soil moisture index (ESSMI), and standardised snow water equivalent index (SWEI), aggregated across various temporal scales. Cross-correlation and event coincidence analysis were applied between these indices and the standardised streamflow index at a temporal scale of 1 month (SSI-1), as representative of streamflow drought events. Our results underscore that there is not a single drought index and temporal scale best suited to characterise all streamflow droughts in Chile, and their suitability largely depends on catchment memory. Specifically, in snowmelt-driven catchments characterised by a slow streamflow response to precipitation, the SPI at accumulation periods of 12–24 months serves as the best proxy for characterising streamflow droughts, with median correlation and coincidence rates of approximately 0.70–0.75 and 0.58–0.75, respectively. In contrast, the SPI at a 3-month accumulation period is the best proxy over faster-response rainfall-driven catchments, with median coincidence rates of around 0.55. Despite soil moisture and snowpack being key variables that modulate the propagation of meteorological deficits into hydrological ones, meteorological indices are better proxies for streamflow drought. Finally, to exclude the influence of non-drought periods, we recommend using the event coincidence analysis, a method that helps assessing the suitability of meteorological, soil moisture, and/or snow drought indices as proxies for streamflow drought events. [ABSTRACT FROM AUTHOR]

In drought-prone Australia, multi-year droughts have detrimental impacts on both the natural environment and human societies. For responsible water management, we need a thorough understanding of the full range of variability in multi-year droughts and how this might change in a warming world. But research into the long-term frequency, persistence, and severity of Australian droughts is limited. This is partly due to the length of the observational record, which is short relative to the timescales of hydroclimatic variability and hence may not capture the range of possible variability. Using simulations of Australian precipitation over the full past millennium (850–2000), we characterise the nature of multi-year meteorological droughts across Australia and include a particular focus on the Murray–Darling Basin (MDB), the largest agricultural region in Australia. We find that simulated Australian droughts in the 20th century (1900–2000) are within the bounds of pre-industrial natural variability in terms of drought intensity, severity, and frequency. A tendency for droughts to last longer in southwestern and eastern Australia (including the MDB) in the 20th century, compared with the pre-industrial period, suggests an emerging anthropogenic influence, consistent with projected rainfall changes in these regions. Large volcanic eruptions tend to promote drought-free intervals in the MDB. Model simulations of droughts over the last millennium suggest that future droughts across Australia could be much longer than what was experienced in the 20th century, even without any human influence. With the addition of anthropogenic climate change, which favours drought conditions across much of southern Australia due to reduced cool-season rainfall, it is likely that future droughts in Australia will exceed recent historical experience. [ABSTRACT FROM AUTHOR]

Clay shrinkage, which consists of a reduction in the volume of clay soils during dry periods, can affect buildings and cause subsidence damage. In France, losses due to subsidence are estimated at more than EUR 16 billion for the period 1989–2021 (CCR, 2021) and are expected to increase under the effect of climate warming. This work aims to improve the current understanding of the conditions triggering subsidence by proposing an innovative drought index. We use a daily soil wetness index (SWI) to develop a new annual drought index that can be related to subsidence damage. The SWI is derived from simulations of soil moisture profiles from the interactions between soil–biosphere–atmosphere (ISBA) land surface model developed by Météo-France. The ability of the drought index to correlate with insurance claim data is assessed by calculating the Kendall rank correlation over 20 municipalities in France. The insurance data, aggregated by year and municipality, are provided by the Caisse Centrale de Réassurance (CCR). A total of 1200 configurations of the drought index are considered. They are generated by combining different calculation methods, ISBA simulation settings, soil model layers, and drought percentile thresholds. The analysis includes a comparison with the independent claim data of six additional municipalities and with a record of official "CatNat" (the French national natural disaster compensation scheme) decrees, useful for the analysis. The best results are obtained for drought magnitudes based on SWI values of the 0.8-to-1.0 m deep soil layer, an ISBA simulation with interactive leaf area index (LAI), and consideration of low drought SWI percentile thresholds. Comparison with claim data shows that drought magnitude is able to identify subsidence events while being spatially consistent. This drought magnitude index provides more insight into subsidence triggers while benefiting from advanced land surface modeling schemes (interactive LAI, multilayer soil). This work paves the way for more reliable damage estimates. [ABSTRACT FROM AUTHOR]