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Human activity has a profound influence on river discharges, hydrological extremes and water-related hazards. In this study, we compare the results of five state-of-the-art global hydrological models (GHMs) with observations to examine the role of human impact parameterizations (HIP) in the simulation of mean, high- and low-flows. The analysis is performed for 471 gauging stations across the globe for the period 1971-2010. We find that the inclusion of HIP improves the performance of the GHMs, both in managed and near-natural catchments. For near-natural catchments, the improvement in performance results from improvements in incoming discharges from upstream managed catchments. This finding is robust across the GHMs, although the level of improvement and the reasons for it vary greatly. The inclusion of HIP leads to a significant decrease in the bias of the long-term mean monthly discharge in 36%-73% of the studied catchments, and an improvement in the modeled hydrological variability in 31%-74% of the studied catchments. Including HIP in the GHMs also leads to an improvement in the simulation of hydrological extremes, compared to when HIP is excluded. Whilst the inclusion of HIP leads to decreases in the simulated high-flows, it can lead to either increases or decreases in the low-flows. This is due to the relative importance of the timing of return flows and reservoir operations as well as their associated uncertainties. Even with the inclusion of HIP, we find that the model performance is still not optimal. This highlights the need for further research linking human management and hydrological domains, especially in those areas in which human impacts are dominant. The large variation in performance between GHMs, regions and performance indicators, calls for a careful selection of GHMs, model components and evaluation metrics in future model applications.

Flooding in the Yangtze River Basin could severely damage socio-economic development, river ecosystems, food security, hydropower production and transportation in China. The Yangtze River Basin accounts for approximately 30% of China’s gross domestic product (GDP) and is an engine for the country’s rapid economic growth. One commonly held belief is that climate change has intensified extreme flood events, leading to increasing economic damage in the Yangtze River. Here, we quantitatively attributed economic exposure to climate change (i.e. climate-induced changes in weather-related events) and GDP growth, and assessed benefits, i.e. the reduction in economic exposure, from flood defence dikes of varying heights. To do this, we developed a framework by combing a large scale hydrological model, a hydraulic model, and long-term GDP data. We find that climate-induced changes in flood inundation area and resulted economic exposure were decreasing overall, whereas GDP growth drove the increases of potential economic exposure to floods. We also reveal that the basin average flood defence dikes should be at least approximately 3.5 m high to achieve an about ten-year average flood occurrence. Our results have significant policy and socioeconomic development implications.

The Tonle Sap Lake (TSL) Basins of the Lower Mekong are one of the world’s most productive ecosystems and have recently been disturbed by climate change. The SWAT (Soil & Water Assessment Tool) hydrological model is utilized to investigate the effect of future climate scenarios. This study focused on two climate scenarios (RCP2.6 and RCP8.5) with three GCMs (GFDL-CM3, GISS-E2-R-CC, and IPSL-CM5A-MR) and their impact on the hydrological process and extremes in the Sen River Basin, the largest tributary of the TSL basin. The annual precipitation, surface runoff, lateral flow, groundwater flow, and total water yield are projected to decrease in both the near-future (2020–2040) and mid-future period (2050–2070), while actual evapotranspiration is projected to increase by 3.3% and 5.3%. Monthly precipitation is projected to increase by 11.2% during the rainy season and decrease by 7.5% during the dry season. Two climate models (GISS and IPSL model) lead to decreases in 1-day, 3-day, 7-day, 30-day, and 90-day maximum flows and minimum flows flow. Thus, the prediction results depend on the climate model used.

Extreme dry and wet events can result in significant crop yield losses. However, the impact of consecutive occurrence of dry and wet extremes on crop yield remains unclear. Here, we investigate the hotspots of compound dry and wet (CDW) extremes across global rice croplands and their impacts on rice yield. We identify a significant increasing trend in the frequency of CDW extremes during 1981–2016. The risk of yield loss caused by CDW extremes can be twice as high as the risk from individual wet and dry extremes. Furthermore, we find that global rice croplands face a 43% higher risk of rice yield loss due to dry‐to‐wet extremes compared to wet‐to‐dry extremes. Our findings provide new insights into the sustainability of global rice production and food security in the face of compound hydrological extremes. Plain Language Summary It is widely recognized that compound events may exert larger impacts on crop production compared to individual extremes. Here, we investigate the consecutive occurrence of dry and wet (CDW) extremes during the rice‐growing season and estimate their impacts on rice yield. We observe a significant increase in the frequency of CDW extremes across global rice croplands during the rice‐growing season from 1981 to 2016. The CDW extremes exert a larger impact on rice yield loss compared to individual wet and dry extremes. The CDW extremes, characterized by longer durations of both dry and wet extremes and faster transitions between them, have an even more adverse influence on rice yield. The risk of yield loss caused by CDW extremes is 200% higher than the risk from individual wet and dry extremes. Furthermore, global rice croplands face a 43% higher risk of yield loss due to dry‐to‐wet extremes than wet‐to‐dry extremes. Key Points A significant increasing trend in the frequency of compound dry and wet (CDW) extremes was observed across global rice croplands The risk of rice yield loss caused by CDW extremes can be twice as high as the risk from individual wet and dry extremes Global rice croplands face a 43% higher risk of rice yield loss caused by dry‐to‐wet extremes compared to wet‐to‐dry extremes

Global hydrological reanalyses are modelled datasets providing information on river discharge evolution everywhere in the world. With multi‐decadal daily timeseries, they provide long‐term context to identify extreme hydrological events such as floods and droughts. By covering the majority of the world's land masses, they can fill the many gaps in river discharge in‐situ observational data, especially in the global South. These gaps impede knowledge of both hydrological status and future evolution and hamper the development of reliable early warning systems for hydrological‐related disaster reduction. River discharge is a natural integrator of the water cycle over land. Global hydrological reanalysis datasets offer an understanding of its spatio‐temporal variability and are therefore critical for addressing the water–energy–food–environment nexus. This paper describes how global hydrological reanalyses can fill the lack of ground measurements by using earth system or hydrological models to provide river discharge time series. Following an inventory of alternative sources of river discharge datasets, reviewing their advantages and limitations, the paper introduces the Copernicus Emergency Management Service (CEMS) Global Flood Awareness System (GloFAS) modelling chain and its reanalysis dataset as an example of a global hydrological reanalysis dataset. It then reviews examples of downstream applications for global hydrological reanalyses, including monitoring of land water resources and ocean dynamics, understanding large‐scale hydrological extreme fluctuations, early warning systems, earth system model diagnostics and the calibration and training of models, with examples from three Copernicus Services (Emergency Management, Marine and Climate Change). Global hydrological reanalyses are powerful datasets that can fill the observational gap in river discharge observation. They make wide ranging downstream applications possible worldwide, from water resources to ocean monitoring and early warning systems, through earth system model diagnostic, hydrological extreme understanding and model calibration and training. The GloFAS hydrological reanalysis dataset is a product of the Copernicus Emergency Management Service freely available from the Copernicus Climate Data store, offering daily time series from early 1980 until recent, updated daily with a 3‐ to 5‐day delay.

In this paper, we evaluated the variability of total continental water storage derived from estimates of balance water using satellite data in association with hydro-meteorological data. The occurrence of extreme hydrological events such as drought and flood in the Amazon basin was related to the variability of total storage of continental water. Both estimation methods (PER- Precipitation, Evapotranspiration and Runoff and GRACE) show a strong decrease in water storage during the 2005 drought and a strong recovery during the 2009 flood. The results show that there is strong relationship between the occurrences of extreme hydrological events and water storage in the Amazon. Local and deep measurements of continental water storage can provide more precise indications of the dynamics of the hydrological system and its response to climate variability.

We introduce a new hydrological index that enables assessment of extreme events every few days from the GRACE Follow‐On (GRACE‐FO) satellite mission. The Mass Change Index (MCI) was developed by standardizing instantaneous satellite gravity anomalies computed directly from orbit perturbations. It is based on hydrology‐related gravity change, namely, total water storage change, and thus equally sensitive to wet and dry anomalies. The key innovation of MCI is its sensitivity to instantaneous mass changes as opposed to monthly mean changes. GRACE‐FO's ground track permits MCI retrievals every 5–6 days in most low and mid latitude regions. We demonstrate the application of MCI to investigate hydrological extremes in the middle‐lower Yangtze River Basin (MLYRB). MCI detects extreme wet conditions (standardized index of 2.0–3.0) along the Yangtze River mainstream related to the catastrophic flood in 2020, consistent with daily streamflow observations. In contrast, a typical GRACE‐FO based monthly drought index significantly underestimates the severity of the event and misidentifies timing of the onset. MCI also detects extreme dry conditions (−2.0 to −2.5) prevailing within MLYRB, related to the unprecedented heatwave and drought event during the summer of 2022. A streamflow index and the monthly drought index both underestimate the severity of the event. MCI retains information in intersatellite range measurements that may be lost when processing monthly gravity solutions. It can also be processed more rapidly, increasing its potential value for hydrological monitoring systems and other operational applications.

The hydrological extremes, caused by increasing regional extreme precipitation and melting glaciers or snow under climate change, pose a major challenge to reservoir management in Tianshan Mountain Range of China. Modeling and assessment of hydrological extremes are important measures to ensure the safety of reservoir operations and regional water resources. However, insufficient assessment of hydrological extremes faced by reservoirs in Tianshan Mountain Range has limited the development of flood risk assessment and early warning methods for mountain reservoirs. To this end, based on the VIC-CAS-R model that coupled with glacier snowmelt and reservoir modules, this study analyzed and evaluated the changing characteristics of precipitation and streamflow of selected mountain reservoirs in Tianshan Mountain Range from 1961 to 2014, and the Standardized Streamflow Index (SSI) and Mann-Kendall test were used to identify hydrological extremes as well as the changing trends. The result indicated that (1) the precipitation and streamflow of reservoirs in Tianshan Mountain Range showed a segmented change trend of “increasing-decreasing-increasing”; (2) The hydrological extremes of reservoirs in Tianshan Mountain Range showed notable variations in temporal and spatial distribution, reservoirs located in the western area faced a decrease in wet hydrological extremes (up to 70.8%) and an increase in dry hydrological extremes (up to 73.9%), while reservoir in the eastern region experienced a simultaneous increase in dry and wet hydrological extremes (up to 119.8%). These insights help to deepen the comprehension of the changing characteristics of hydrological extremes induced by climate change in Tianshan Mountain Range reservoirs, and provide support for predicting hydrological extremes in other arid inland mountain regions.

Floods following on streamflow droughts can have severe impacts. While they have been prominently featured by the media in recent years, we know little about their spatio‐temporal variability. In this study, we analyze the occurrence and drivers of such drought‐to‐flood transitions by calculating transition lengths from droughts to floods for natural and regulated catchments across the Contiguous United States between 1970 and 2022. We find that drought‐to‐flood transitions strongly vary in their lengths and their spatial distribution. We identify snowmelt as the main driver of transitions in high‐elevation catchments, while transitions in low‐elevation catchments are more variable in their time of occurrence and drivers. Reservoir management reduces the number of short drought‐to‐flood transitions, particularly in catchments with a high amount of snow where snowmelt is crucial for filling reservoirs in early summer. These findings suggest that projected changes in the snowmelt season will lead to changes in transitions from streamflow droughts to floods and that reservoir management may be used to adapt to these changes. Key Points Transitions from streamflow droughts to floods are driven by snowmelt in high‐elevation catchments The seasonality and length of drought‐to‐flood transitions varies widely across the United States, with transitions happening in all seasons Reservoir management can reduce the number of drought‐to‐flood transitions and be used for adaptation purposes