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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.

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.

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.

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.

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.

The Variable Infiltration Capacity (VIC) model is a macroscale semi-distributed hydrologic model. VIC development began in the early 1990s and the model has since been used extensively for basin- to global-scale applications that include hydrologic dataset construction, trend analysis of hydrologic fluxes and states, data evaluation and assimilation, forecasting, coupled climate modeling, and climate change impact assessment. Ongoing operational applications of the VIC model include the University of Washington's drought monitoring and forecasting systems and NASA's Land Data Assimilation System. This paper documents the development of VIC version 5 (VIC-5), which includes a major reconfiguration of the legacy VIC source code to support a wider range of modern hydrologic modeling applications. The VIC source code has been moved to a public GitHub repository to encourage participation by the broader user and developer communities. The reconfiguration has separated the core physics of the model from the driver source code, whereby the latter is responsible for memory allocation, preprocessing and post-processing, and input–output (I–O). VIC-5 includes four drivers that use the same core physics modules, but which allow for different methods for accessing this core to enable different model applications. Finally, VIC-5 is distributed with robust test infrastructure, components of which routinely run during development using cloud-hosted continuous integration. The work described here provides an example to the model development community for extending the life of a legacy model that is being used extensively. The development and release of VIC-5 represents a significant step forward for the VIC user community in terms of support for existing and new model applications, reproducibility, and scientific robustness.

Hydrological models can provide estimates of streamflow pre- and post-observations, which enable greater understanding of past hydrological behaviour, and potential futures. In this paper, a new multi-objective calibration method was derived and tested for 303 catchments in the UK, and the calibrations were used to reconstruct river flows back to 1891, in order to provide a much longer view of past hydrological variability, given the brevity of most UK river flow records which began post-1960. A Latin hypercube sample of 500 000 parameterisations for the GR4J model for each catchment were evaluated against six evaluation metrics covering all aspects of the flow regime from high, median, and low flows. The results of the top ranking model parameterisation (LHS1), and also the top 500 (LHS500), for each catchment were used to provide a deterministic result whilst also accounting for parameter uncertainty. The calibrations are generally good at capturing observed flows, with some exceptions in heavily groundwater-dominated catchments, and snowmelt and artificially influenced catchments across the country. Reconstructed flows were appraised over 30-year moving windows and were shown to provide good simulations of flow in the early parts of the record, in cases where observations were available. To consider the utility of the reconstructions for drought simulation, flow data for the 1975–1976 drought event were explored in detail in nine case study catchments. The model's performance in reproducing the drought events was found to vary by catchment, as did the level of uncertainty in the LHS500. The Standardised Streamflow Index (SSI) was used to assess the model simulations' ability to simulate extreme events. The peaks and troughs of the SSI time series were well represented despite slight over- or underestimations of past drought event magnitudes, while the accumulated deficits of the drought events extracted from the SSI time series verified that the model simulations were overall very good at simulating drought events. This paper provides three key contributions: (1) a robust multi-objective model calibration framework for calibrating catchment models for use in both general and extreme hydrology; (2) model calibrations for the 303 UK catchments that could be used in further research, and operational applications such as hydrological forecasting; and (3) ∼ 125 years of spatially and temporally consistent reconstructed flow data that will allow comprehensive quantitative assessments of past UK drought events, as well as long-term analyses of hydrological variability that have not been previously possible, thus enabling water resource managers to better plan for extreme events and build more resilient systems for the future.

Precipitation is a crucial driver of hydrological processes. Ironically, a reliable characterization of its spatiotemporal variability is challenging. Ground-based rainfall measurement using rain gauges is more accurate. However, installing a dense gauging network to capture rainfall variability can be impractical. Satellite-based rainfall estimates (SREs) could be good alternatives, especially for data-scarce basins like in Ethiopia. However, SRE rainfall is plagued with uncertainties arising from many sources. The objective of this study was to evaluate the performance of the latest versions of several SRE products (i.e., CHIRPS2, IMERG6, TAMSAT3 and 3B42/3) for the Dhidhessa River Basin (DRB). Both statistical and hydrological modeling approaches were used for the performance evaluation. The Soil and Water Analysis Tool (SWAT) was used for hydrological simulations. The results showed that whereas all four SRE products are promising to estimate and detect rainfall for the DRB, the CHIRPS2 dataset performed the best at annual, seasonal and monthly timescales. The hydrological simulation-based evaluation showed that SWAT's calibration results are sensitive to the rainfall dataset. The hydrological response of the basin is found to be dominated by the subsurface processes, primarily by the groundwater flux. Overall, the study showed that both CHIRPS2 and IMERG6 products could be reliable rainfall data sources for the hydrological analysis of the DRB. Moreover, the climatic season in the DRB influences rainfall and streamflow estimation. Such information is important for rainfall estimation algorithm developers.

This paper provides a description of the integrated representation for the cloud processes in the Community Atmosphere Model, version 5 (CAM5). CAM5 cloud parameterizations add the following unique characteristics to previous versions: 1) a cloud macrophysical structure with horizontally nonoverlapped deep cumulus, shallow cumulus, and stratus in each grid layer, where each of which has its own cloud fraction, and mass and number concentrations for cloud liquid droplets and ice crystals; 2) stratus–radiation–turbulence interactions that allow CAM5 to simulate marine stratocumulus solely from grid-mean relative humidity without relying on a stability-based empirical formula; 3) prognostic treatment of the number concentrations of stratus liquid droplets and ice crystals, with activated aerosols and detrained in-cumulus condensates as the main sources and with evaporation, sedimentation, and precipitation of stratus condensate as the main sinks; and 4) radiatively active cumulus and snow. By imposing consistency between diagnosed stratus fraction and prognosed stratus condensate, unrealistically empty or highly dense stratus is avoided in CAM5. Because of the activation of the prognostic aerosols and the parameterizations of the radiation and stratiform precipitation production as a function of the cloud droplet size, CAM5 simulates various aerosol indirect effects as well as the direct effects: that is, aerosols affect both the radiation budget and the hydrological cycle. Detailed analysis of various simulations indicates that CAM5 improves upon CAM3/CAM4 in global performance as well as in physical formulation. However, several problems are also identified in CAM5, which can be attributed to deficient regional tuning, inconsistency between various physics parameterizations, and incomplete treatment of physics. Efforts are continuing to further improve CAM5.

In traditional watershed delineation and topographic modeling, surface depressions are generally treated as spurious features and simply removed from a digital elevation model (DEM) to enforce flow continuity of water across the topographic surface to the watershed outlets. In reality, however, many depressions in the DEM are actual wetland landscape features with seasonal to permanent inundation patterning characterized by nested hierarchical structures and dynamic filling–spilling–merging surface-water hydrological processes. Differentiating and appropriately processing such ecohydrologically meaningful features remains a major technical terrain-processing challenge, particularly as high-resolution spatial data are increasingly used to support modeling and geographic analysis needs. The objectives of this study were to delineate hierarchical wetland catchments and model their hydrologic connectivity using high-resolution lidar data and aerial imagery. The graph-theory-based contour tree method was used to delineate the hierarchical wetland catchments and characterize their geometric and topological properties. Potential hydrologic connectivity between wetlands and streams were simulated using the least-cost-path algorithm. The resulting flow network delineated potential flow paths connecting wetland depressions to each other or to the river network on scales finer than those available through the National Hydrography Dataset. The results demonstrated that our proposed framework is promising for improving overland flow simulation and hydrologic connectivity analysis.