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A new version of a conceptual climate model of river runoff was used to calculate daily and monthly hydrographs for the Lena R. basin with the use of MERRA reanalysis data and data on runoff from R-ArcticNet archive over a 32-year period (1980–2011). The optimization of model parameters and corrections to precipitation allowed the authors to obtain a good quality of the calculated runoff hydrographs and to reveal a trend in the long-term runoff dynamics over the historical period since 1985 to 2011. The period 1985–2011 shows an abrupt increase in the surface air temperature, an increase in precipitation in the Arctic region since the mid-1980s, in particular, in the Lena River basin. The climate changes have led to changes in the hydrological regime of the river basin, and the question of assessing the long-term dynamics of water discharges over this period, in particular, using the conceptual model, gains in importance.

Long-term variations in the water balance were analyzed in the basins of 20 largest tributaries of the Baikal. The values of river runoff were derived from actual data on the period from 1976 to 2019, and those of evaporation, precipitation, and potential evaporation, from ERA5-Land reanalysis since 1976 to 2020. Data were obtained to show an increase in the potential evaporation in all analyzed drainage basins by values from 0.39 to 0.62% per year since 1976 to 2020. A moderate or statistically insignificant decrease is typical of precipitation (0.25 to 0.59% per year) and water discharges, mostly due to a decrease in the summer runoff at a rate of 5.6%/10 years. The possible role of changes in vegetation cover in these processes was studied by evaluating NDVI parameter in 2019 compared with 2002 by data of space surveys MOD13A3 and MYD13A3 with MODIS spectroradiometer of Terra and Aqua satellites. A conclusion was made about the key role of precipitation in the decrease in the maximal runoff in Lake Baikal basin.

Estimating the frequency and magnitude of natural hazards largely hinges on stationary models, which do not account for changes in the climatological, hydrological, and geophysical baseline conditions. Using five diverse case studies encompassing various natural hazard types, we present advanced statistical and machine learning methods to analyze and model transient states from long‐term inventory data. A novel storminess metric reveals increasing European winter windstorm severity from 1950 to 2010. Non‐stationary extreme value models quantify trends, seasonal shifts, and regional differences in extreme precipitation for Germany between 1941 and 2021. Utilizing quantile sampling and empirical mode decomposition on 148 years of daily weather and discharge data in the European Alps, we assess the impacts of changing snow cover, precipitation, and anthropogenic river network modifications on river runoff. Moreover, a probabilistic framework estimates return periods of glacier lake outburst floods in the Himalayas, demonstrating large differences in 100‐year flood levels. Utilizing a Bayesian change point algorithm, we track the onset of increased seismicity in the southern central United States and find correlation with wastewater injections into deep wells. In conclusion, data science reveals transient states for very different natural hazard types, characterized by diverse forms of change, ranging from gradual trends to sudden change points and from altered seasonality to overall intensity variations. In synergy with the physical understanding of Earth science, we gain important new insights into the dynamics of the studied hazards and their possible mechanisms. Plain Language Summary According to global databases on natural hazard events and associated risks, there has been a noteworthy escalation in the extent of economic losses during past decades. It is important but difficult to distinguish and disentangle trends due to changing hazard occurrence or damage potential. Accurately quantifying altered hazards requires high‐quality data sets and robust statistical methodologies. Here, we present recent progress in earth and data science toward a quantitative assessment of natural hazards in a changing world. We show that winter storms have become more frequent and more severe in Europe; that extreme precipitation in Germany shows seasonal shifts and changing intensities with regional variation; that river runoff in Central Europe is changing due to modifications of the river network, declining snowpacks, and changes in precipitation; that frequency of glacier lake outburst floods in the Himalayas have remained unchanged over the past 30 years despite rapid glacier melt and lake growth; and that earthquake activity in Oklahoma (USA) has increased with the onset of wastewater injection wells. We infer that recent advances in data science can efficiently provide new knowledge from big data sets, but interpreting these results needs a solid understanding and rather detailed analysis of the underlying processes. Key Points Time‐dependent approaches are key to capture changing hazards, with diverse altered frequencies, intensities, timing or spatial occurrences Advances in data‐driven methods and increasing availability of geodata foster new ways to detect changes in natural hazards Over‐simplification or averaging over large scales in time or space cause severe information loss and may hide the mechanisms for change

A regional numerical physico-mathematical model of river runoff formation is used to study the possibility to assess long-term variations of water regime characteristics in the Amur R. in the XXI century. Two methods were used to specify climate projections as boundary conditions in the hydrological model: (1) based on the results of calculations with an ensemble of global climate models of CMI5 project, (2) based on data obtained by linear transformation of series of actual meteorological observations with the use of normal annual climate parameters calculated by climate models. The results of numerical experiments were used to analyze the sensitivity of the anomaly of Amur normal annual runoff to changes in the climate normals of air temperature and precipitation. The anomalies of normal annual runoff were shown to respond similarly (within the accuracy of sensitivity coefficient estimates) to changes in the appropriate climate normals, whatever the way of specifying climate projections.

Three river basins—the Lena, Ganges, and Darling—were selected to study the possibility of reproducing water balance components of river basins, located in different regions of the globe under a wide variety of natural conditions, with the use of the land surface model SWAP and global data sets. Input data including meteorological forcings and land surface parameters were prepared on the basis of the WATCH and ECOCLIMAP global data sets, respectively. Long-term variations of the water balance components of the Lena, Ganges, and Darling river basins were simulated by the SWAP model. The results of simulations were compared with observations. In addition, the natural variability of river runoff caused by the weather noise of atmospheric characteristics was estimated.

Monitoring changes in river runoff at the Third Pole (TP) is important because rivers in this region support millions of inhabitants in Asia and are very sensitive to climate change. Under the influence of climate change and intensified cryospheric melt, river runoff has changed markedly at the TP, with significant effects on the spatial and temporal water resource distribution that threaten water supply and food security for people living downstream. Despite some in situ observations and discharge estimates from state-of-the-art remote sensing technology, the total river runoff (TRR) for the TP has never been reliably quantified, and its response to climate change remains unclear. As part of the Chinese Academy of Sciences’ “Pan-Third Pole Environment Study for a Green Silk Road,” the TP-River project aims to construct a comprehensive runoff observation network at mountain outlets (where rivers leave the mountains and enter the plains) for 13 major rivers in the TP region, thereby enabling TRR to be accurately quantified. The project also integrates discharge estimates from remote sensing and cryosphere–hydrology modeling to investigate long-term changes in TRR and the relationship between the TRR variations and westerly/monsoon. Based on recent efforts, the project provides the first estimate (656 ± 23 billion m³) of annual TRR for the 13 TP rivers in 2018. The annual river runoff at the mountain outlets varies widely between the different TP rivers, ranging from 2 to 176 billion m³, with higher values mainly corresponding to rivers in the Indian monsoon domain, rather than in the westerly domain.

Compound events (CEs) are multivariate extreme events in which the individual contributing variables may not be extreme themselves, but their joint – dependent – occurrence causes an extreme impact. Conventional univariate statistical analysis cannot give accurate information regarding the multivariate nature of these events. We develop a conceptual model, implemented via pair-copula constructions, which allows for the quantification of the risk associated with compound events in present-day and future climate, as well as the uncertainty estimates around such risk. The model includes predictors, which could represent for instance meteorological processes that provide insight into both the involved physical mechanisms and the temporal variability of compound events. Moreover, this model enables multivariate statistical downscaling of compound events. Downscaling is required to extend the compound events' risk assessment to the past or future climate, where climate models either do not simulate realistic values of the local variables driving the events or do not simulate them at all. Based on the developed model, we study compound floods, i.e. joint storm surge and high river runoff, in Ravenna (Italy). To explicitly quantify the risk, we define the impact of compound floods as a function of sea and river levels. We use meteorological predictors to extend the analysis to the past, and get a more robust risk analysis. We quantify the uncertainties of the risk analysis, observing that they are very large due to the shortness of the available data, though this may also be the case in other studies where they have not been estimated. Ignoring the dependence between sea and river levels would result in an underestimation of risk; in particular, the expected return period of the highest compound flood observed increases from about 20 to 32 years when switching from the dependent to the independent case.

Projections of possible changes in streamflow of three northern rivers (the Northern Dvina, Kolyma, and Indigirka) up to 2100 were calculated for two greenhouse gas emission scenarios: a high emissions scenario (RCP8.5) and a medium mitigation scenario (RCP4.5) used for the fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). For each scenario, several hydrological projections were obtained using different models (AOGCM INMCM4.0 and LSM SWAP) and different post-processing techniques for correcting biases in meteorological forcing data simulated by INMCM4.0 and used to drive the SWAP model. Uncertainties in river runoff projections associated with the application of different emission scenarios, different models, and bias-correction techniques were estimated and analyzed.

We review the characteristics of sea level variability at the coast focussing on how it differs from the variability in the nearby deep ocean. Sea level variability occurs on all timescales, with processes at higher frequencies tending to have a larger magnitude at the coast due to resonance and other dynamics. In the case of some processes, such as the tides, the presence of the coast and the shallow waters of the shelves results in the processes being considerably more complex than offshore. However, ‘coastal variability’ should not always be considered as ‘short spatial scale variability’ but can be the result of signals transmitted along the coast from 1000s km away. Fortunately, thanks to tide gauges being necessarily located at the coast, many aspects of coastal sea level variability can be claimed to be better understood than those in the deep ocean. Nevertheless, certain aspects of coastal variability remain under-researched, including how changes in some processes (e.g., wave setup, river runoff) may have contributed to the historical mean sea level records obtained from tide gauges which are now used routinely in large-scale climate research.

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.