Explore the vast range of titles available.

In the wake of global and regional climate change and heightened human activities, runoff from some rivers in the world, especially in the arid and semi-arid regions, has significantly decreased. To reveal the varying characteristics leading to the change in runoff, detecting the influencing factors has been important in recent scientific discussions for water resources management in drainage basins. In this paper, an investigation into attributing the runoff response to climate change and human activities were conducted in two catchments (Wushan and Shetang), situated in the upper reaches of Weihe River in China. Prior to the identification of the factors that influenced runoff changes, the Mann–Kendall test was adopted to identify the trends in hydro-climate series. Also, change-points in the annual runoff were detected through Pettitt’s test and the precipitation–runoff double cumulative curve method. It is found that both catchments presented significant negative trend in annual runoff and the detected change-point in runoff occurs in 1993. Hence, the pre-change period and post-change period are defined before and after 1993, respectively. Then, runoff response to climate change and human activities was quantitatively evaluated on the basis of hydrologic sensitivity analysis and hydrologic model simulation. They provided similar estimates of the percentage change in mean annual runoff for the post-change period over the considered catchments. It is found that the decline in annual runoff over both catchments can be mainly attributed to the human activities, the reduction percentages due to human activities range from 59 to 77 %. The results of this study can provide a reference for the development, utilization and management of the regional water resources and ecological environment protection.

In 2022, Europe faced an extensive summer drought with severe socioeconomic consequences. Quantifying the influence of human-induced climate change on such an extreme event can help prepare for future droughts. Here, by combining observations and climate model outputs with hydrological and land-surface simulations, we show that Central and Southern Europe experienced the highest observed total water storage deficit since satellite observations began in 2002, probably representing the highest and most widespread soil moisture deficit in the past six decades. While precipitation deficits primarily drove the soil moisture drought, human-induced global warming contributed to over 30% of the drought intensity and its spatial extent via enhanced evaporation. We identify that 14–41% of the climate change contribution was mediated by the warming-driven drying of the soil that occurred before the hydrological year of 2022, indicating the importance of considering lagged climate change effects to avoid underestimating associated risks. Human-induced climate change had qualitatively similar effects on the extremely low observed river discharges. These results highlight that global warming effects on droughts are already underway, widespread and long lasting, and that drought risk may escalate with further human-induced warming in the future. An attribution analysis using observations, hydrological models and climate models suggests that both direct and lagged effects of climate warming contributed to Europe experiencing the highest observed water storage deficit in the satellite era during the widespread drought of 2022.

Flooding due to overtopping during peak flow in embankment dams primarily causes dam failure. The Kessem River watershed of the Awash basin in the Rift Valley of the Afar region in Ethiopia was studied intricately to predict the causes of the Kessem Dam safety using machine learning predictive models and risk management centre-reservoir frequency analysis. Recently developed recurrent neural network predictive models with hybrid Soil Conservation Service Curve Number (SCS-CN) were used for simulation of river flow. Peak daily inflow to the reservoir is predicted to be 467.72, 435.88, and 513.55 m3/s in 2035, 2061, and 2090, respectively. The hydrologic hazard analysis results show 2,823.57 m3/s and 935.21 m; 2,126.3 m3/s and 934.18 m; and 11,491.1 m3/s and 942.11 m peak discharge and maximum reservoir water level during the periods of 2022–2050, 2051–2075, and 2076–2100, respectively, for 0.0001 annual exceedance probability. The Kessem Dam may potentially be overtopped by a flood with a return period of about 10,000 years during the period of 2076–2100. Quantitative hydrologic risk assessment of the dam is used for dam safety evaluation to decide whether the existing structure provides an adequate level of safety, and if not, what modifications are necessary to improve the dam's safety.

The Indus basin heavily depends on its upstream mountainous part for the downstream supply of water while downstream demands are high. Since downstream demands will likely continue to increase, accurate hydrological projections for the future supply are important. We use an ensemble of statistically downscaled CMIP5 General Circulation Model outputs for RCP4.5 and RCP8.5 to force a cryospheric-hydrological model and generate transient hydrological projections for the entire 21st century for the upper Indus basin. Three methodological advances are introduced: (i) A new precipitation dataset that corrects for the underestimation of high-altitude precipitation is used. (ii) The model is calibrated using data on river runoff, snow cover and geodetic glacier mass balance. (iii) An advanced statistical downscaling technique is used that accounts for changes in precipitation extremes. The analysis of the results focuses on changes in sources of runoff, seasonality and hydrological extremes. We conclude that the future of the upper Indus basin's water availability is highly uncertain in the long run, mainly due to the large spread in the future precipitation projections. Despite large uncertainties in the future climate and long-term water availability, basin-wide patterns and trends of seasonal shifts in water availability are consistent across climate change scenarios. Most prominent is the attenuation of the annual hydrograph and shift from summer peak flow towards the other seasons for most ensemble members. In addition there are distinct spatial patterns in the response that relate to monsoon influence and the importance of meltwater. Analysis of future hydrological extremes reveals that increases in intensity and frequency of extreme discharges are very likely for most of the upper Indus basin and most ensemble members.

Heavy precipitation over western Germany and neighboring countries in July 2021 led to widespread floods, with the Ahr and Erft river catchments being particularly affected. Following the event characterization and process analysis in Part 1, here we put the 2021 event in the historical context regarding precipitation and discharge records and in terms of the temporal transformation of the valley morphology. Furthermore, we evaluated the role of ongoing and future climate change on the modification of rainfall totals and the associated flood hazard, as well as implications for flood management. The event was among the five heaviest precipitation events of the past 70 years in Germany. However, consideration of the large LAERTES-EU regional climate model (RCM) ensemble revealed a substantial underestimation of both return levels and periods based on extreme value statistics using only observations. An analysis of homogeneous hydrological data of the last 70 years demonstrated that the event discharges exceeded by far the statistical 100-year return levels. Nevertheless, the flood peaks at the Ahr river were comparable to the reconstructed major historical events of 1804 and 1910, which were not included in the flood risk assessment so far. A comparison between the 2021 and past events showed differences in terms of the observed hydro-morphodynamic processes which enhanced the flood risk due to changes in the landscape organization and occupation. The role of climate change and how the 2021 event would unfold under warmer or colder conditions (within a −2 to +3 K range) was considered based on both a pseudo global warming (PGW) model experiments and the analysis of an RCM ensemble. The PGW experiments showed that the spatial mean precipitation scales with the theoretical Clausius–Clapeyron (CC) relation, predicting a 7 % to 9 % increase per degree of warming. Using the PGW rainfall simulations as input to a hydrological model of the Ahr river basin revealed a strong and non-linear effect on flood peaks: for the +2 K scenario, the 18 % increase in areal rainfall led to a 39 % increase of the flood peak at gauge Altenahr. The analysis of the high-resolution convection-permitting KIT-KLIWA RCM ensemble confirmed the CC scaling for moderate spatial mean precipitation but showed a super CC scaling of up to 10 % for higher intensities. Moreover, the spatial extent of such precipitation events is also expected to increase.

The study of sediment‐riverflow interactions during discrete hydrological events is vital for enhancing our understanding of the hydrological cycle. Hysteresis analysis, relying on high‐resolution, continuous monitoring of suspended sediment concentration (SSC) and discharge (Q) data, is an effective tool for investigating complex hydrological events. It captures differing sediment dynamic at the same discharge level, which results from the asynchrony between the hydrograph and sediment graph during different phases of the event. However, there has been no comprehensive review systematically addressing the utility and significance of hysteresis analysis in soil and water management. This review synthesizes findings from over 500 global studies, providing a detailed examination of current research. We trace the development and application of hysteresis analysis in hydrology, illustrating its role in classifying and characterizing events, as well as uncovering sediment sources and transport mechanisms. Furthermore, hysteresis analysis has proven effective in identifying critical hydrological events, offering valuable insights for targeted watershed management. Our spatiotemporal analysis of global hysteresis research shows that over 70% of studies are located in semi‐arid and Mediterranean climate zones, with an increasing focus on alpine and tropical regions due to climate change. This review also highlights critical limitations, including the scarcity of high‐resolution data, inconsistent use of quantitative indices, and limited integration of hysteresis patterns into predictive hydrological approaches. Future research should focus on developing region‐specific hydrological models that incorporate hysteresis dynamics, along with standardizing methodologies to apply hysteresis analysis across diverse climatic and geomorphic settings. Key Points We review hysteresis methods and identify global hotspots for analyzing suspended sediment dynamic during discrete hydrological events Combining qualitative and quantitative hysteresis methods effectively classify events and reveal sediment sources and transport processes Hysteresis methods face challenges in accuracy and applicability under the growing complexity of future extreme events

Extreme flooding impacts millions of people that live within the Amazon floodplain. Global hydrological models (GHMs) are frequently used to assess and inform the management of flood risk, but knowledge on the skill of available models is required to inform their use and development. This paper presents an intercomparison of eight different GHMs freely available from collaborators of the Global Flood Partnership (GFP) for simulating floods in the Amazon basin. To gain insight into the strengths and shortcomings of each model, we assess their ability to reproduce daily and annual peak river flows against gauged observations at 75 hydrological stations over a 19-year period (1997–2015). As well as highlighting regional variability in the accuracy of simulated streamflow, these results indicate that (a) the meteorological input is the dominant control on the accuracy of both daily and annual maximum river flows, and (b) groundwater and routing calibration of Lisflood based on daily river flows has no impact on the ability to simulate flood peaks for the chosen river basin. These findings have important relevance for applications of large-scale hydrological models, including analysis of the impact of climate variability, assessment of the influence of long-term changes such as land-use and anthropogenic climate change, the assessment of flood likelihood, and for flood forecasting systems.

Hydrological analysis utilizing a hydrological model requires a parameter calibration process, which is largely influenced by the length of calibration data period and prevailing hydrological conditions. This study aimed to quantify these uncertainties in future runoff projection and hydrological drought based on future climate data and the calibration data of the hydrological model. Future climate data were sourced from three Shared Socioeconomic Pathway (SSP) scenarios (SSP2-4.5, SSP3-7.0, and SSP5-8.5) of 20 general circulation models (GCMs). The Soil and Water Assessment Tool (SWAT) was employed as the hydrological model, and hydrological conditions were determined using the Streamflow Drought Index (SDI), with calibration data lengths ranging from 1 to 20 years considered. Subsequently, the uncertainty was quantified using Analysis of Variance (ANOVA). After calibrating SWAT parameters, the validation performance was found to be influenced by the hydrological conditions of the calibration data. Hydrological model parameters calibrated using a dry period simulated runoff with 11.4 % higher performance in dry conditions and 6.1 % higher performance in normal conditions, while hydrological model parameters calibrated using a wet period simulated runoff with 5.1 % higher performance in wet conditions. While the ANOVA results confirmed that GCMs are the dominant source of total uncertainty, the uncertainty contribution from the hydrological model calibration in estimating future runoff was analyzed to be 3.9 %–9.8 %, particularly significant in the low runoff period. The uncertainty contribution in future hydrological drought analysis resulting from the calibration of hydrological model parameters was analyzed to be 2.7 % on average, which is lower than that of future runoff projection.

Alpine basins are important water sources for human life, and reliable hydrological modeling can enhance the water resource management in alpine basins. Recently, hybrid hydrological models, coupling process-based models and deep learning (DL), have exhibited considerable promise in hydrological simulations. However, a notable limitation of existing hybrid models lies in their failure to incorporate spatial information within the basin and describe alpine hydrological processes, which restricts their applicability in hydrological modeling in large alpine basins. To address this issue, we develop a set of hybrid semi-distributed hydrological models by employing a process-based model as the backbone and utilizing embedded neural networks (ENNs) to parameterize and replace different internal modules. The proposed models are tested on three large alpine basins on the Tibetan Plateau. A climate perturbation method is further used to test the applicability of the hybrid models to analyze the hydrological sensitivities to climate change in large alpine basins. Results indicate that proposed hybrid hydrological models can perform well in predicting runoff processes and simulating runoff component contributions in large alpine basins. The optimal hybrid model with Nash–Sutcliffe efficiencies (NSEs) higher than 0.87 shows comparable performance to state-of-the-art DL models. The hybrid model also exhibits remarkable capability in simulating hydrological processes at ungauged sites within the basin, markedly surpassing traditional distributed models. In addition, the results also show reasonable patterns in the analysis of the hydrological sensitivities to climate change. Overall, this study provides a high-performance tool enriched with explicit hydrological knowledge for hydrological prediction and improves our understanding about the hydrological sensitivities to climate change in large alpine basins.

The hydrological system of the Rift Valley Lakes in Ethiopia has recently experienced changes over the past 2 decades. Potential causes for these changes include anthropogenic, hydro-climatic, and geological factors. The main objective of this study was to utilize an integrated methodology to gain a comprehensive understanding of the hydrological systems and potential driving factors within a complex and data-scarce region. To this end, we integrated a hydrologic model, change point analysis, indicators of hydrological alteration (IHA), and a bathymetry survey to investigate hydrological dynamics and potential causes. A hydrologic model (the Soil and Water Assessment Tool Plus, SWAT+) was parameterized for the gauged watersheds and extended to the ungauged watersheds using multi-site regionalization techniques. The SWAT+ model performed very well to satisfactorily for daily streamflow in all watersheds with respect to the objective functions of the Kling–Gupta efficiency (KGE), the Nash–Sutcliffe efficiency (NSE), and percent bias (PBIAS). The findings reveal notable changes in lake inflows and lake levels over the past 2 decades. Lake Chamo experienced an increase in area of 30.1 km2 (9.5 %), an increase in depth of 4.4 m (30.9 %), and an increase in volume of 7.8×108 m3 (27.2 %). In contrast, Lake Abijata witnessed an extraordinary 68 % decrease in area and a depth decrease of 1.6 m (37.2 %). During the impact period, the mean annual rainfall experienced a decrease of 6.5 % and 2.7 % over Lake Abijata and Lake Chamo, respectively. Actual evapotranspiration decreased by 2.9 % in Lake Abijata but increased by up to 4.5 % in Lake Chamo. Surface inflow to Lake Abijata decreased by 12.5 %, while Lake Chamo experienced an 80.5 % increase in surface inflow. Sediment depth in Lake Chamo also increased by 0.6 m (4.2 %). The results highlight that the changing hydrological regime in Lake Chamo is driven by increased surface runoff and sediment intrusion associated with anthropogenic influences. The hydrological regime of Lake Abijata is affected by water abstraction from feeding rivers and lakes for industrial and irrigation purposes. This integrated methodology provides a holistic understanding of complex data-scarce hydrological systems and potential driving factors in the Rift Valley Lakes in Ethiopia, which could have global applicability.