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.

A climate transition towards warm-wet conditions in Northwest China has drawn much attention. With continuous climate change and universal glacier degradation, increasing water-related hazards and vulnerability have become one of the important problems facing the Tarim Basin. However, the impacts of the climate transition on streamflow abrupt change and extreme hydrological events were less discussed, especially in glacial basins. In the present study, the discharge datasets in four glacial basins of Tarim Basin from 1979 to 2018 were constructed using the GRU-GSWAT thorn model first. The differences in streamflow characteristics, the shift of hydrological extreme pattern, and potential changes of the controlling factors before and after the abrupt changes were investigated. The results indicated that the abrupt change point (ACP) in streamflow occurred in 2000 in the Qarqan River Basin, 2002 in the Weigan River Basin, and 1994 in the Aksu River Basin and the Yarkant River Basin. A general decrease in streamflow before the ACP has shifted to a notable upward trend in the Qarqan River Basin and the Aksu River Basin, while minor upward fluctuations were observed in other basins. Moreover, the hydrological characteristics in extreme events vary dramatically before and after the ACPs, characterized by a pronouncing shift from drought-dominant pattern to wet events dominated pattern. The driven climate factors have been altered after the ACPs with notable spatial heterogeneity, in which temperature remained as the dominant role in meltwater-dominated basins while the influence of precipitation has increased after the ACPs, whereas the sensitivity of temperature on streamflow change has been enhanced in basins dominated by precipitation such as the Qarqan River Basin. Owing to the evident warming-wetting trend and glacier compensation effect, both the inter-annual and intra-annual streamflow fluctuations can be efficiently smoothed in basins with a high glacier area ratio (GAR). These findings provide a further understanding of the abrupt change in streamflow under the exacerbated climate and glacier change in mountainous arid regions.

Determining the age and sources of stream water is critical for understanding the watershed hydrological processes and biogeochemical cycle. In this study, daily isotope data of rainfall and runoff, as well as continuously monitored conductivity data from June to October in 2019 in-Laoyeling(LYL) watershed located in permafrost region of northeastern China were used to separate streamflow components through the application of two independent methods: isotope-based hydrograph separations (IHS) and the conductivity mass balance (CMB) methods. The results showed that stream water in a boreal forest watershed with permafrost of the Daxing'an Mountains is mainly composed of pre-event water. Although the IHS method is more sensitive and provides more details than the CMB method, the results of both methods show a similar trend. The average value of the young water fractions (Fyw) for those aged less than 65 days is 5.6%, while the mean transit time (MTT) was calculated to be 3.33 years. These findings enhance our understanding of the fundamental characteristics of runoff generation mechanisms and changes in runoff components in permafrost regions. Such knowledge is crucial for effective regional water resource management under the context of climate change, such as construction of water conservancy facilities and prediction of flood and drought disasters.

Robust streamflow simulation at glacial basins is essential for the improvement of water sustainability assessment, water security evaluation, and water resource management under the rapidly changing climate. Therefore, we proposed a hybrid modelling framework to link the SWAT+ model considering glacial hydrological processes (GSWAT+) with Gated Recurrent Unit (GRU) neural networks to improve the model simulations and to establish a framework for the robust simulation and forecast of high and low flows in glacial river basins, which could be further used for the explorations of extreme hydrological events under a warming climate. The performance of different models (GSWAT+, GRU, and GRU-GSWAT+, respectively) were thoroughly investigated based on numerical experiments for two data-scarce glacial watersheds in Northwest China. The results suggested that the hybrid model (GRU-GSWAT+) outperformed both the individual deep learning (DL) model (GRU) and the conventional hydrological model (GSWAT+) in terms of simulation and prediction accuracy. Notably, the proposed hybrid model considerably enhanced the simulations of low and high flows that the conventional GSWAT+ failed to capture. Furthermore, utilizing suitable data integration (DI) schemes on feature and target sequences can substantially help to strengthen model stability and representativeness for monthly and annual streamflow sequences. Specifically, introducing one order differential method and decomposition approach, such as ensemble empirical signal decomposition (EEMD) and complete EEMD with adaptive noise (CEEMDAN), into feature and target sequences enriched the learnable ancillary information, which consequently strengthened the predictive performance of the proposed model. Overall, the proposed hybrid model with the suitable DI scheme has the potential to significantly enhance the accuracy of streamflow simulation in data-scarce glacial river basins. This hybrid model not only upheld the fundamental physical principles from the GSWAT+ model, but also considerably mitigated the accumulated bias errors, which caused by the shortage of climate data and inadequate hydrological principles, by using DL based model and DI schemes.

Arctic hydrology is experiencing rapid changes including earlier snow melt, permafrost degradation, increasing active layer depth, and reduced river ice, all of which are expected to lead to changes in stream flow regimes. Recently, long-term (>60 years) climate reanalysis and river discharge observation data have become available. We utilized these data to assess long-term changes in discharge and their hydroclimatic drivers. River discharge during the cold season (October-April) increased by 10% per decade. The most widespread discharge increase occurred in April (15% per decade), the month of ice break-up for the majority of basins. In October, when river ice formation generally begins, average monthly discharge increased by 7% per decade. Long-term air temperature increases in October and April increased the number of days above freezing (+1.1 d per decade) resulting in increased snow ablation (20% per decade) and decreased snow water equivalent (-12% per decade). Compared to the historical period (1960-1989), mean April and October air temperature in the recent period (1990-2019) have greater correlation with monthly discharge from 0.33 to 0.68 and 0.0-0.48, respectively. This indicates that the recent increases in air temperature are directly related to these discharge changes. Ubiquitous increases in cold and shoulder-season discharge demonstrate the scale at which hydrologic and biogeochemical fluxes are being altered in the Arctic.

Since the middle of the 20th century, the peak snowpack in the Upper Rio Grande (URG) basin of United States has been decreasing. Warming influences snowpack characteristics such as snow cover, snow depth, and Snow Water Equivalent (SWE), which can affect runoff quantity and timing in snowmelt runoff-dominated river systems of the URG basin. The purpose of this research is to investigate which variables are most important in predicting naturalized streamflow and to explore variables' relative importance for streamflow dynamics. We use long term remote sensing data for hydrologic analysis and deploy R algorithm for data processing and synthesizing. The data is analyzed on a monthly and baseflow/runoff basis for nineteen sub-watersheds in the URG. Variable importance and influence on naturalized streamflow is identified using linear standard regression with multi-model inference based on the second-order Akaike information criterion (AICc) coupled with the intercept only model. Five predictor variables: temperature, precipitation, soil moisture, sublimation, and SWE are identified in order of relative importance for streamflow prediction. The most influential variables for streamflow prediction vary temporally between baseflow and runoff conditions and spatially by watershed and mountain range. Despite the importance of temperature on streamflow, it is not consistently the most important factor in streamflow prediction across time and space. The dominance of precipitation over streamflow is more obvious during baseflow. The impact of precipitation, SWE, sublimation, and minimum temperature on streamflow is evident during the runoff season, but the results vary for different sub-watersheds. The association between sublimation and streamflow is positive in the runoff season, which may relate to temperature and requires further research. This research sheds light on the primary drivers and their spatial and temporal variability on streamflow generation. This work is critical for predicting how warming temperatures will impact water supplies serving society and ecosystems in a changing climate.

This study investigates the impacts of climate change on the hydrology and soil thermal regime of 10 sub-arctic watersheds (northern Manitoba, Canada) using the Variable Infiltration Capacity (VIC) model. We utilize statistically downscaled and biascorrected forcing datasets based on 17 general circulation model (GCM) - representative concentration pathways (RCPs) scenarios from phase 5 of the Coupled Model Intercomparison Project (CMIP5) to run the VIC model for three 30-year periods: a historical baseline (1981-2010: 1990s), and future projections (2021-2050: 2030s and 2041-2070: 2050s), under RCPs 4.5 and 8.5. Future warming increases the average soil column temperature by similar to 2.2 C in the 2050s and further analyses of soil temperature trends at three different depths show the most pronounced warming in the top soil layer (1.6 degrees C 30-year(-1) in the 2050s). Trend estimates of mean annual frozen soil moisture fraction in the soil column show considerable changes from 0.02 30-year(-1) (1990s) to 0.11 30-year(-1) (2050s) across the study area. Soil column water residence time decreases significantly (by 5 years) during the 2050s when compared with the 1990s as soil thawing intensifies the infiltration process thereby contributing to faster conversion to baseflow. Future warming results in 40%-50% more baseflow by the 2050s, where it increases substantially by 19.7% and 46.3% during the 2030s and 2050s, respectively. These results provide crucial information on the potential future impacts of warming soil temperatures on the hydrology of sub-arctic watersheds in north-central Canada and similar hydro-climatic regimes.

Multivariate data assimilation (DA), a novel way to couple big data with land surface models, was extensively employed in forecasting-reanalyzing systems (FRSs), for example, ECMWF and GLDAS. Meanwhile, most (distributed) hydrological models, like soil and water assessment tool (SWAT), have not been equipped with straightforward ways to link to DA algorithms. Therefore, it is one of the main barriers to utilizing such hydrological models in FRSs. This paper deals with multivariate DA into SWAT (DA-SWAT), which is complicated since the original model does not provide full access to the models' initial conditions (ICs) at the hydrologic response unit (HRU) scale. The preceding DA-SWAT works commonly used an integrated approach in which the DA and SWAT codes were implemented in the same programming environment. We discuss how this approach complicates and prevents the application of DA-SWAT in multivariate, multimodel, and multisensor systems. Accordingly, we proposed a new approach for DA-SWAT by which SWAT can be perfectly linked with any DA algorithm of interest coded in any desired programming environment. Our framework utilizes input/output text files to access ICs and to link DA with SWAT. Moreover, we designed some univariate and multivariate scenarios for assimilating in situ streamflow measurement and MODIS's snow cover fraction (SCF) data, which has not yet been focused on in the SWAT calibration context. Results show that compared to the univariate assimilation of streamflow (SCF), the multivariate assimilation mitigates the equifinality problem and more accurately estimates SCF (streamflow) by improving NS and PBIAS measures with the differences of 0.4 (0.86), 12% (64%), respectively.

The increasing winter streamflow of major Arctic rivers has been well documented. However, the contribution of climate change to winter streamflow and associated mechanisms of streamflow generation during early, mid- and late winter are not fully understood. Among the Arctic rivers, we selected four rivers with relatively few dam effects (Lena, Kolyma, Yukon and Mackenzie rivers) and analysed their climate change-related responses in streamflow during early, mid-, and late winter. Our results showed that the winter streamflow (Qwin) of the Lena, Kolyma, Yukon and Mackenzie rivers increased from 1980 to 2019 by approximately 43%, 72%, 16% and 16% (1.7-5.2 times greater than increases in annual streamflow), respectively. In general, the rate of streamflow increase was the greatest in early winter, followed by mid- and late winter. The streamflow in late winter was particularly sensitive to air temperature changes, and permafrost degradation due to rising temperatures is likely a major factor driving late winter streamflow increases. In contrast to late winter streamflow, the larger rate of increase in early winter streamflow can be mainly attributed to the additional influence of increased late summer precipitation on streamflow generation. The different change rates in winter streamflow among the four river basins are highly determined by permafrost degradation and related baseflow discharge processes. Under warming climate conditions, winter streamflow generation is strongly associated with the enhanced hydrological cycle that is apparent in both the surface (e.g., precipitation and river ice) and the subsurface (the active layer and groundwater discharge).