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Climate change poses a significant threat to global biodiversity, but freshwater fishes have been largely ignored in climate change assessments. Here, we assess threats of future flow and water temperature extremes to ~11,500 riverine fish species. In a 3.2 °C warmer world (no further emission cuts after current governments’ pledges for 2030), 36% of the species have over half of their present-day geographic range exposed to climatic extremes beyond current levels. Threats are largest in tropical and sub-arid regions and increases in maximum water temperature are more threatening than changes in flow extremes. In comparison, 9% of the species are projected to have more than half of their present-day geographic range threatened in a 2 °C warmer world, which further reduces to 4% of the species if warming is limited to 1.5 °C. Our results highlight the need to intensify (inter)national commitments to limit global warming if freshwater biodiversity is to be safeguarded. Climate change is a threat to global biodiversity, but the potential effects on freshwater fishes have not been well studied. Here the authors model future flow and water temperature extremes and predict that increases in water temperature in particular will pose serious threats to freshwater fishes

Present-day drought early warning systems provide the end-users information on the ongoing and forecasted drought hazard (e.g. river flow deficit). However, information on the forecasted drought impacts, which is a prerequisite for drought management, is still missing. Here we present the first study assessing the feasibility of forecasting drought impacts, using machine-learning to relate forecasted hydro-meteorological drought indices to reported drought impacts. Results show that models, which were built with more than 50 months of reported drought impacts, are able to forecast drought impacts a few months ahead. This study highlights the importance of drought impact databases for developing drought impact functions. Our findings recommend that institutions that provide operational drought early warnings should not only forecast drought hazard, but also impacts after developing an impact database. There still lacks a forecast system that inform end-users regarding the drought impacts, which will be however important for drought management. Here the authors assess the feasibility of forecasting drought impacts using machine-learning and confirm that models, which were built with sufficient amount of reported drought impacts in a certain sector, are able to forecast drought impacts a few months ahead.

There is growing evidence that climate change will alter water availability in Europe. Here, we investigate how hydrological low flows are affected under different levels of future global warming (i.e. 1.5, 2, and 3 K with respect to the pre-industrial period) in rivers with a contributing area of more than 1000 km2. The analysis is based on a multi-model ensemble of 45 hydrological simulations based on three representative concentration pathways (RCP2.6, RCP6.0, RCP8.5), five Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCMs: GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, NorESM1-M) and three state-of-the-art hydrological models (HMs: mHM, Noah-MP, and PCR-GLOBWB). High-resolution model results are available at a spatial resolution of 5 km across the pan-European domain at a daily temporal resolution. Low river flow is described as the percentile of daily streamflow that is exceeded 90 % of the time. It is determined separately for each GCM/HM combination and warming scenario. The results show that the low-flow change signal amplifies with increasing warming levels. Low flows decrease in the Mediterranean region, while they increase in the Alpine and Northern regions. In the Mediterranean, the level of warming amplifies the signal from −12 % under 1.5 K, compared to the baseline period 1971–2000, to −35 % under global warming of 3 K, largely due to the projected decreases in annual precipitation. In contrast, the signal is amplified from +22 (1.5 K) to +45 % (3 K) in the Alpine region due to changes in snow accumulation. The changes in low flows are significant for regions with relatively large change signals and under higher levels of warming. However, it is not possible to distinguish climate-induced differences in low flows between 1.5 and 2 K warming because of (1) the large inter-annual variability which prevents distinguishing statistical estimates of period-averaged changes for a given GCM/HM combination, and (2) the uncertainty in the multi-model ensemble expressed by the signal-to-noise ratio. The contribution by the GCMs to the uncertainty in the model results is generally higher than the one by the HMs. However, the uncertainty due to HMs cannot be neglected. In the Alpine, Northern, and Mediterranean regions, the uncertainty contribution by the HMs is partly higher than those by the GCMs due to different representations of processes such as snow, soil moisture and evapotranspiration. Based on the analysis results, it is recommended (1) to use multiple HMs in climate impact studies and (2) to embrace uncertainty information on the multi-model ensemble as well as its single members in the adaptation process.

Sub-seasonal to seasonal weather and hydrological forecasts have the potential to provide vital information for a variety of water-related decision makers. Here, we investigate the skill of four sub-seasonal forecast models from phase-2 of the North American Multi-Model Ensemble using reforecasts for the period 1982-2012. Two weighted multi-model ensemble means from the models have been developed for predictions of both sub-seasonal precipitation and temperature. By combining models through optimal weights, the multi-model forecast skill is significantly improved compared to a 'standard' equally weighted multi-model forecast mean. We show that optimal model weights are robust and the forecast skill is maintained for increased length of time and regions with a low initial forecast skill show significant skill after optimal weighting of the individual model forecast. The sub-seasonal model forecasts models show high skill over the tropics, approximating their skill at monthly resolution. Using the weighted approach, a significant increase is found in the forecast skill for dry, wet, cold and warm extreme events. The weighted mean approach brings significant advances to sub-seasonal forecasting due to its reduced uncertainty in the forecasts with a gain in forecast skill. This significantly improves their value for end-user applications and our ability to use them to prepare for upcoming extreme conditions, like floods and droughts.

We present PCR-GLOBWB 2, a global hydrology and water resources model. Compared to previous versions of PCR-GLOBWB, this version fully integrates water use. Sector-specific water demand, groundwater and surface water withdrawal, water consumption, and return flows are dynamically calculated at every time step and interact directly with the simulated hydrology. PCR-GLOBWB 2 has been fully rewritten in Python and PCRaster Python and has a modular structure, allowing easier replacement, maintenance, and development of model components. PCR-GLOBWB 2 has been implemented at 5 arcmin resolution, but a version parameterized at 30 arcmin resolution is also available. Both versions are available as open-source codes on https://github.com/UU-Hydro/PCR-GLOBWB_model (Sutanudjaja et al., 2017a). PCR-GLOBWB 2 has its own routines for groundwater dynamics and surface water routing. These relatively simple routines can alternatively be replaced by dynamically coupling PCR-GLOBWB 2 to a global two-layer groundwater model and 1-D–2-D hydrodynamic models. Here, we describe the main components of the model, compare results of the 30 and 5 arcmin versions, and evaluate their model performance using Global Runoff Data Centre discharge data. Results show that model performance of the 5 arcmin version is notably better than that of the 30 arcmin version. Furthermore, we compare simulated time series of total water storage (TWS) of the 5 arcmin model with those observed with GRACE, showing similar negative trends in areas of prevalent groundwater depletion. Also, we find that simulated total water withdrawal matches reasonably well with reported water withdrawal from AQUASTAT, while water withdrawal by source and sector provide mixed results.

Severe river floods often result in huge economic losses and fatalities. Since 1980, almost 1500 such events have been reported in Europe. This study investigates climate change impacts on European floods under 1.5, 2, and 3 K global warming. The impacts are assessed employing a multi-model ensemble containing three hydrologic models (HMs: mHM, Noah-MP, PCR-GLOBWB) forced by five CMIP5 general circulation models (GCMs) under three Representative Concentration Pathways (RCPs 2.6, 6.0, and 8.5). This multi-model ensemble is unprecedented with respect to the combination of its size (45 realisations) and its spatial resolution, which is 5 km over the entirety of Europe. Climate change impacts are quantified for high flows and flood events, represented by 10% exceedance probability and annual maxima of daily streamflow, respectively. The multi-model ensemble points to the Mediterranean region as a hotspot of changes with significant decrements in high flows from −11% at 1.5 K up to −30% at 3 K global warming mainly resulting from reduced precipitation. Small changes (< ±10%) are observed for river basins in Central Europe and the British Isles under different levels of warming. Projected higher annual precipitation increases high flows in Scandinavia, but reduced snow melt equivalent decreases flood events in this region. Neglecting uncertainties originating from internal climate variability, downscaling technique, and hydrologic model parameters, the contribution by the GCMs to the overall uncertainties of the ensemble is in general higher than that by the HMs. The latter, however, have a substantial share in the Mediterranean and Scandinavia. Adaptation measures for limiting the impacts of global warming could be similar under 1.5 K and 2 K global warming, but have to account for significantly higher changes under 3 K global warming.

Rivers and the organisms living within them are highly vulnerable to hot thermal extremes. However, very little is known about river heatwaves, consecutive episodes of anomalously high temperature in rivers, and how they may evolve under climate change. Here we show that river heatwaves will become more intense and more persistent globally by the end of the 21 st century, with some tropical rivers reaching a persistent year-round heatwave state in the early 21 st century. Under the high-greenhouse-gas-emission scenario (Representative Concentration Pathway 8.5), the average intensity of river heatwaves will increase by ~4.2-fold, and the average duration by ~95-fold, relative to the baseline period (1976–2005). Nearly half of the world’s rivers are expected to experience a year-round heatwave state by the 2090 s. Global population exposure to river heatwaves will reach 16.8 billion person-weeks annually, with a disproportionately heavier burden on vulnerable low-income regions, such as the Congo River basin. Emerging persistent river heatwaves may push river ecosystems and aquatic organisms to their resilience limits, causing irreversible changes and widespread impacts. River heatwaves are becoming stronger and longer-lasting globally. Nearly half of the world’s rivers will reach a ‘permanent’ (year-round) heatwave state by the 2090 s under high greenhouse gas emissions scenario, and annual population exposure will reach 16.8 billion person-weeks.

Global freshwater resources are vital to humanity and Earth’s ecosystems, yet about one third of the global population is affected by water scarcity for at least one month per year. In these areas, the overuse of freshwater resources can lead to the threat of depletion, marking them as the global ‘water scarcity hotspots’. This study combines outputs from a global hydrological model (PCR-GLOBWB 2) with an extensive literature search to provide a comprehensive intercomparison of the key drivers, pressures, states, impacts and responses (DPSIR) that shape the water gap between water demand and availability at the most important water scarcity hotspots worldwide. Hydroclimatic change, population growth, and water use for the industrial, municipal and agricultural sectors are the most important driving and pressuring forces on the water gap, affecting both water quality and quantity. These drivers and pressures have been showing increasing trends at all hotspots, which is concerning for the future development of the water gap. Additionally, we identify and characterize seven clusters of hotspots based on shared DPSIR patterns, revealing their common mechanisms. Our work highlights the diversity of water scarcity related issues at hotspots, especially the variety of impacts involved and governmental responses in place. The results of our DPSIR analysis provide valuable insights for building causal networks representing water gap dynamics at the hotspots. They form a foundation for conceptual models that illuminate human-water interactions, trade-offs, and synergies at the hotspots, while guiding policymakers in addressing the multifaceted challenge of closing the water gap.

Over the past 50 years, human water use has more than doubled and affected streamflow over various regions of the world. However, it remains unclear to what degree human water consumption intensifies hydrological drought (the occurrence of anomalously low streamflow). Here, we quantify over the period 1960-2010 the impact of human water consumption on the intensity and frequency of hydrological drought worldwide. The results show that human water consumption substantially reduced local and downstream streamflow over Europe, North America and Asia, and subsequently intensified the magnitude of hydrological droughts by 10-500%, occurring during nation- and continent-wide drought events. Also, human water consumption alone increased global drought frequency by 27 (±6)%. The intensification of drought frequency is most severe over Asia (35 ± 7%), but also substantial over North America (25 ± 6%) and Europe (20 ± 5%). Importantly, the severe drought conditions are driven primarily by human water consumption over many parts of these regions. Irrigation is responsible for the intensification of hydrological droughts over the western and central US, southern Europe and Asia, whereas the impact of industrial and households' consumption on the intensification is considerably larger over the eastern US and western and central Europe. Our findings reveal that human water consumption is one of the more important mechanisms intensifying hydrological drought, and is likely to remain as a major factor affecting drought intensity and frequency in the coming decades.

The summer of 2018 was characterized by high temperatures and low precipitation values in the Netherlands. The drought negatively impacted different sectors, resulting in an estimated damage of 450 to 2080 million Euros. Strong regional differences were observed in the precipitation shortfall across the country, with highest deficits in the southern and eastern regions. This raised two questions: (i) have increasing global temperatures contributed to changes in meteorological and agricultural droughts as severe or worse as in 2018? And (ii) are trends in these types of droughts different for coastal and inland regions? In this paper we show that there is no trend in summer drought (Apr-Sep) near the coast. However, a trend in agricultural drought is observed for the inland region where water supply is mainly dependent on local precipitation. This trend is driven by strong trends in temperature and global radiation rather than a trend in precipitation, resulting in an overall trend in potential evapotranspiration. Climate model analyses confirm that this trend in agricultural drought can at least in part be attributed to global climate change.