Climate change impacts water supply dynamics in the Upper Rio Grande (URG) watersheds of the US Southwest, where declining snowpack and altered snowmelt patterns have been observed. While temperature and precipitation effects on streamflow often receive the primary focus, other hydroclimate variables may provide more specific insight into runoff processes, especially at regional scales and in mountainous terrain where snowpack is a dominant water storage. The study addresses the gap by examining the mechanisms of generating streamflow through multi-modal inferences, coupling the Bayesian Information Criterion (BIC) and Bayesian Model Averaging (BMA) techniques. We identified significant streamflow predictors, exploring their relative influences over time and space across the URG watersheds. Additionally, the study compared the BIC-BMA-based regression model with Random Forest Regression (RFR), an ensemble Machine Learning (RFML) model, and validated them against unseen data. The study analyzed seasonal and long-term changes in streamflow generation mechanisms and identified emergent variables that influence streamflow. Moreover, monthly time series simulations assessed the overall prediction accuracy of the models. We evaluated the significance of the predictor variables in the proposed model and used the Gini feature importance within RFML to understand better the factors driving the influences. Results revealed that the hydroclimate drivers of streamflow exhibited temporal and spatial variability with significant lag effects. The findings also highlighted the diminishing influence of snow parameters (i. e., snow cover, snow depth, snow albedo) on streamflow while increasing soil moisture influence, particularly in downstream areas moving towards upstream or elevated watersheds. The evolving dynamics of snowmelt-runoff hydrology in this mountainous environment suggest a potential shift in streamflow generation pathways. The study contributes to the broader effort to elucidate the complex interplay between hydroclimate variables and streamflow dynamics, aiding in informed water resource management decisions.

In the mountainous headwaters of the Colorado River episodic dust deposition from adjacent arid and disturbed landscapes darkens snow and accelerates snowmelt, impacting basin hydrology. Patterns and impacts across the heterogenous landscape cannot be inferred from current in situ observations. To fill this gap daily remotely sensed retrievals of radiative forcing and contribution to melt were analyzed over the MODIS period of record (2001-2023) to quantify spatiotemporal impacts of snow darkening. Each season radiative forcing magnitudes were lowest in early spring and intensified as snowmelt progressed, with interannual variability in timing and magnitude of peak impact. Over the full record, radiative forcing was elevated in the first decade relative to the last decade. Snowmelt was accelerated in all years and impacts were most intense in the central to southern headwaters. The spatiotemporal patterns motivate further study to understand controls on variability and related perturbations to snow water resources.

Alpine vegetation, cold deserts, and glacial landscapes significantly impact runoff generation and convergence in cold and alpine regions. The presence of existing mountain permafrost complicates these impacts further. To better understand the specific regulation of runoff by alpine landscapes, we analyzed the spatiotemporal capacity for runoff generation and the contributions of water from different landscape types within a typical alpine permafrost watershed: the upper reaches of the Shule River (USR) basin in the Qinghai-Tibet Plateau. The analysis was informed by both field observations and simulations using the VIC model, which incorporated a new glacier module. We identified that glaciers, alpine meadows, cold deserts, and barren landscape zones as the four major runoff generation regions, collectively accounting for approximately 95 % of the USR runoff. The runoff depth in each landscape zone was calculated to express its runoff generation capacity, with an order of: glacier > cold desert > barren > alpine grassland > alpine meadow > shrub > swamp meadow. The alpine regions above 4000 m in altitude are the primary runoff generation areas, and the runoff generation capacity gradually decreases from high to low altitudes in the alpine basin. Due to seasonal variations in rainfall distribution, glacier melting, and permafrost thawing-freezing, the dominant landscape types contributing to runoff varied monthly. The simulated results indicate that permafrost plays an important role in runoff generation. Although permafrost degradation had a slight impact on the annual total runoff generated from each landscape zone (not taking into account of ground ice), seasonal runoff generated in each landscape exhibited significant changes in response to permafrost thawing. After permafrost completely thawed in each landscape zone, generated flood flow decreased, while low flow conversely increased, implying an enhanced water retention capacity of alpine landscapes following permafrost degradation. Additionally, the responses of runoff to permafrost changes varied across different alpine landscapes. These findings enhance our understanding of the mechanisms underlying runoff generation and convergence in cold and alpine watersheds of the Northern Hemisphere.

River-controlled permafrost dynamics are crucial for sediment transport, infrastructure stability, and carbon cycle, yet are not well understood under climate change. Leveraging remotely sensed datasets, in-situ hydrological observations, and physics-based models, we reveal overall warming and widening rivers across the Tibetan Plateau in recent decades, driving accelerated sub-river permafrost thaw. River temperature of a representative (Tuotuohe River) on the central Tibetan Plateau, has increased notably (0.39 degrees C/decade) from 1985 to 2017, facilitating heat transfer into the underlying permafrost via both convection and conduction. Consequently, the permafrost beneath rivers warms faster (0.37 degrees C-0.66 degrees C/decade) and has a similar to 0.5 m thicker active layer than non-inundated permafrost (0.17 degrees C-0.49 degrees C/decade). With increasing river discharge, the inundated area expands laterally along the riverbed (16.4 m/decade), further accelerating permafrost thaw for previously non-inundated bars. Under future warmer and wetter climate, the anticipated intensification of sub-river permafrost degradation will pose risks to riverine infrastructure and amplify permafrost carbon release.

This study shows the impact of black carbon (BC) aerosol atmospheric rivers (AAR) on the Antarctic Sea ice retreat. We detect that a higher number of BC AARs arrived in the Antarctic region due to increased anthropogenic wildfire activities in 2019 in the Amazon compared to 2018. Our analyses suggest that the BC AARs led to a reduction in the sea ice albedo, increased the amount of sunlight absorbed at the surface, and a significant reduction of sea ice over the Weddell, Ross Sea (Ross), and Indian Ocean (IO) regions in 2019. The Weddell region experienced the largest amount of sea ice retreat (similar to 33,000 km(2)) during the presence of BC AARs as compared to similar to 13,000 km(2) during non-BC days. We used a suite of data science techniques, including random forest, elastic net regression, matrix profile, canonical correlations, and causal discovery analyses, to discover the effects and validate them. Random forest, elastic net regression, and causal discovery analyses show that the shortwave upward radiative flux or the reflected sunlight, temperature, and longwave upward energy from the earth are the most important features that affect sea ice extent. Canonical correlation analysis confirms that aerosol optical depth is negatively correlated with albedo, positively correlated with shortwave energy absorbed at the surface, and negatively correlated with Sea Ice Extent. The relationship is stronger in 2019 than in 2018. This study also employs the matrix profile and convolution operation of the Convolution Neural Network (CNN) to detect anomalous events in sea ice loss. These methods show that a higher amount of anomalous melting events were detected over the Weddell and Ross regions. Impact Statement Sea ice protects ice sheets, which are melting at a very high rate to raise the sea level. In addition to global warming, this study is indicative that black carbon aerosols transported from anthropogenic wildfire events, such as from the Amazon, darken the snow, reduce their reflectance, increase the absorption of solar energy incident on the surface, and exacerbate the sea ice retreat. Thus, this study points out that anthropogenic wildfire impacts far away from a region can have a severe impact on sea ice and ice sheets over the Antarctic which has a sea level rise potential of 60 m. Our study shows that only over the Weddell region, sea ice retreat was 20,000 km(2) higher during the presence of BC transport events than other days in 2019.

Study region: Urumqi River headwater region in eastern Tianshan, central Asia. Study focus: Climate change is anticipated to accelerate glacier shrinkage and alter hydrological conditions, causing variations in the runoff patterns in the catchment and significantly threatening the regional water resources. However, few models exhibit adequate performance to simulate both surface alterations and glacier/snow runoff. Therefore, this study combined the glacier module with the Soil and Water Assessment Tool (SWAT) model to estimate the effect of climate change on the streamflow in the Urumqi River headwater region. The Urumqi River Headwater region is representative because of its long data series, viatal location, and local water availability, and it contains the longest-observed reference glacier (Urumqi Glacier No.1) in China, which spans the period from 1958 to the present. New hydrological insights for the region: The SWAT model performed satisfactorily for both calibration (1983-2005) and validation (2006-2016) periods with a Nash-Sutcliffe efficiency (NSE) greater than 0.80. The water balance analysis suggested that the snow/glacier melt contributed approximately 25% to the water yield. At the end of the 21st century, the temperature would increase by 2.4-3.8 degrees C while the precipitation would decrease by 1-2% under two future scenarios (ssp245 and ssp585). Thus, a 34-36% reduction in streamflow was projected due to above climate change impacts. This information would contribute to the development of adaptation strategies for sustainable water resource management.

Hydrologic-land surface models (H-LSMs) offer a physically-based framework for representing and predicting the present and future states of the extensive high-latitude permafrost areas worldwide. Their primary challenge, however, is that soil temperature data are severely limited, and traditional model validation, based only on streamflow, can show the right fit to these data for the wrong reasons. Here, we address this challenge by (1) collecting existing data in various forms including in-situ borehole data and different large-scale permafrost maps in addition to streamflow data, (2) comprehensively evaluating the performance of an H-LSM with a wide range of possible process parametrizations and initializations, and (3) assessing possible trade-offs in model performance in concurrently representing hydrologic and permafrost dynamics, thereby pointing to the possible model deficiencies that require improvement. As a case study, we focus on the sub-arctic Liard River Basin in Canada, which typifies vast northern sporadic and discontinuous permafrost regions. Our findings reveal that different process parameterizations tend to align with different data sources or variables, which largely exhibit inconsistencies among themselves. We further observe that a model may fail to represent permafrost occurrence yet seemingly fit streamflows adequately. Nonetheless, we demonstrate that accurately representing essential permafrost dynamics, including the active soil layer and insulation effects from snow cover and soil organic matter, is crucial for developing high-fidelity models in these regions. Given the complexity of processes and the incompatibility among different data sources/variables, we conclude that employing an ensemble of carefully designed model parameterizations is essential to provide a reliable picture of the current conditions and future spatio-temporal co-evolution of hydrology and permafrost.

Study region: The source area of the Yangtze River, a typical catchment in the cryosphere on the Tibet Plateau, was used to develop and validate a distributed hydrothermal coupling model. Study focus: Climate change has caused significant changes in hydrological processes in the cryosphere, and related research has become hot topic. The source area of the Yangtze River (SAYR) is a key catchment for studies of hydrological processes in the cryosphere, which contains widespread glacier, snow, and permafrost. However, the current hydrological modeling of the SAYR rarely depicts the process of glacier/snow and permafrost runoff from the perspective of coupled water and heat transfer, resulting in distortion of simulations of hydrological processes. Therefore, we developed a distributed hydrothermal coupling model, namely WEP-SAYR, based on the WEP-L (Water and energy transfer process in large river basins) model by introducing modules for glacier and snow melt and permafrost freezing and thawing. New hydrological insights for the region: In the WEP-SAYR model, the soil hydrothermal transfer equations were improved, and a freezing point equation for permafrost was introduced. In addition, the glacier and snow meltwater processes were described using the temperature index model. Compared to previously applied models, the WEP-SAYR portrays in more detail glacier/ snow melting, dynamic changes in permafrost water and heat coupling, and runoff dynamics, with physically meaningful and easily accessible model parameters. The model can describe the soil temperature and moisture changes in soil layers at different depths from 0 to 140 cm. Moreover, the model has a good accuracy in simulating the daily/monthly runoff and evaporation. The Nash-Sutcliffe efficiency exceeded 0.75, and the relative error was controlled within +/- 20 %. The results showed that the WEP-SAYR model balances the efficiency of hydrological simulation in large scale catchments and the accurate portrayal of the cryosphere elements, which provides a reference for hydrological analysis of other catchments in the cryosphere.

There is 78 % permafrost and seasonal frozen soil in the Yangtze River's Source Region (SRYR), which is situated in the middle of the Qinghai-Xizang Plateau. Three distinct scenarios were developed in the Soil and Water Assessment Tool (SWAT) to model the effects of land cover change (LCC) on various water balance components. Discharge and percolation of groundwater have decreased by mid-December. This demonstrates the seasonal contributions of subsurface water, which diminish when soil freezes. During winter, when surface water inputs are low, groundwater storage becomes even more critical to ensure water supply due to this periodic trend. An impermeable layer underneath the active layer thickness decreases GWQ and PERC in LCC + permafrost scenario. The water transport and storage phase reached a critical point in August when precipitation, permafrost thawing, and snowmelt caused LATQ to surge. To prevent waterlogging and save water for dry periods, it is necessary to control this peak flow phase. Hydrological processes, permafrost dynamics, and land cover changes in the SRYR are difficult, according to the data. These interactions enhance water circulation throughout the year, recharge of groundwater supplies, surface runoff, and lateral flow. For the region's water resource management to be effective in sustaining ecohydrology, ensuring appropriate water storage, and alleviating freshwater scarcity, these dynamics must be considered.

Winter baseflow (WB) can stabilize freshwater inputs and has important impacts on nutrient migration and the water cycle of a specific region and the oceans. This study systematically analyzed the WB variations in fourteen major Eurasian rivers and found they all had commonly increasing trends (except the Yellow River), with the mean increase ratio of 53.0% (+/- 34.8%, confidence interval 95%) over the past 100 years (the longest time series is 1879-2015). Relative to Northern Eurasia (60 degrees N-70 degrees N) and Southern Eurasia (30 degrees N-40 degrees N), the river WB in middle Eurasia (40 degrees N-60 degrees N) had the largest increase rate (0.60%/year). The increases of the WB in Northern Eurasia and Southern Eurasia have speeded up since the 1990s; on the contrary, they have slowed down or even turned to a decreasing trend after the 1990s in the middle Eurasian rivers. Using multiple linear regression analysis, the quantitative relationship between WB and winter surface air temperature (max, mean and min), snowfall, soil temperature, antecedent precipitation, as well as the river-ice dynamic were determined. We found that the winter air temperature, especially the minimum air temperature was one major factor accounting for WB variation in Eurasia over the past century. When the winter air temperature rises, this leads a reduction in the thickness and volume of river ice, and thus decreases water storage in river ice and leads to an increase in the WB. About 19.6% (6.7%-41.5%) of the winter WB increase in rivers of Siberia was caused by the decreased river ice during the past 100 years. Although groundwater recharge was the dominant reason for WB change, the role of river ice should not be ignored in hydrological study of cold regions.